",isbn:"978-1-80356-966-6",printIsbn:"978-1-80356-965-9",pdfIsbn:"978-1-80356-967-3",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"f86a9f720cc3ac0f1c385d0367ea89b9",bookSignature:"Dr. Fiaz Ahmad and Prof. Muhammad Sultan",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11624.jpg",keywords:"Agricultural Waste, Reuse, Reduction, Soil Health, Recycling, Agriculture and Environment, Modelling and Simulation, Agro-Industrial Waste, Bioresource Processing, Processing and Management, Crop Residue, Forest Waste",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 8th 2022",dateEndSecondStepPublish:"June 16th 2022",dateEndThirdStepPublish:"August 15th 2022",dateEndFourthStepPublish:"November 3rd 2022",dateEndFifthStepPublish:"January 2nd 2023",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Fiaz Ahmad is a researcher in the field of Agricultural Engineering with fifteen years of field and academic experience, currently in charge of the Agricultural Machinery Design Laboratory at Bahauddin Zakariya University. He applied for two patents at the national level.",coeditorOneBiosketch:"A renowned researcher in the field of Agricultural Engineering with 14 years of academic experience at Bahauddin Zakariya University. Winner of various prestigious fellowships, awards, and research grants. Published 250+ articles along with several books and chapters. Guest editor of seven ISI-SCI journals for publishers like SAGE, MDPI, and Frontiers.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"338219",title:"Dr.",name:"Fiaz",middleName:null,surname:"Ahmad",slug:"fiaz-ahmad",fullName:"Fiaz Ahmad",profilePictureURL:"https://mts.intechopen.com/storage/users/338219/images/system/338219.png",biography:"Dr. Fiaz Ahmad is an assistant professor and lecturer at the Department of Agricultural Engineering, Bahauddin Zakariya University, Multan, Pakistan. He obtained his Ph.D. in Agricultural Bioenvironmental and Energy Engineering from Nanjing Agriculture University, China, in 2015, and completed his postdoctorate in Agricultural Engineering from Jiangsu University, Zhenjiang, China, in 2020. He was awarded a fellowship from the Higher Education Commission of Pakistan for Ph.D. studies and from the Chinese Government for post-doctoral studies. He earned a BSc and MSc (Hons) in Agricultural Engineering from the University of Agriculture, Faisalabad, Pakistan, in 2004 and 2007, respectively. He is the author of more than fifty journal and conference articles. He has supervised six master’s students to date, and is currently supervising six master and two doctoral students. Dr. Ahmad has completed three research projects with his research interest focusing on the design of agricultural machinery, agricultural waste management, artificial intelligence (AI), and agricultural bioenvironment.",institutionString:"Bahauddin Zakariya University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Bahauddin Zakariya University",institutionURL:null,country:{name:"Pakistan"}}}],coeditorOne:{id:"199381",title:"Prof.",name:"Muhammad",middleName:null,surname:"Sultan",slug:"muhammad-sultan",fullName:"Muhammad Sultan",profilePictureURL:"https://mts.intechopen.com/storage/users/199381/images/system/199381.png",biography:"Muhammad Sultan is an Assistant Professor at the Department of Agricultural\r\nEngineering, Bahauddin Zakariya University, Multan (Pakistan). He completed his Ph.D.\r\nand Postdoc from Kyushu University (Japan) in the field of Energy & Environmental\r\nEngineering. He was an awardee of MEXT and JASSO fellowships (from the Japanese\r\nGovernment) during Ph.D. and Postdoc studies, respectively. He also did a Postdoc as\r\na Canadian Queen Elizabeth Advance Scholar at Simon Fraser University (Canada) in\r\nthe field of Mechatronic Systems Engineering. He worked for Kyushu University\r\nInternational Institute for Carbon-Neutral Energy Research (WPI-I2CNER) for two years.\r\nCurrently, he is working on 4 research projects funded by the Higher Education\r\nCommission (HEC) of Pakistan. He has completed six projects in past in the field of\r\nagricultural engineering. He has supervised 10+ M.Eng. and Ph.D. thesis and 10+\r\nstudents are currently working under his supervision. He has published 120+ journal\r\narticles, 100+ conference articles, 13 book chapters, and 6 books. He is serving as guest\r\neditor for the journals like Sustainability (MDPI), Agriculture (MDPI), Energies (MDPI),\r\nAdvances in Mechanical Engineering (SAGE), Frontiers in Mechanical Engineering, and\r\nEvergreen Journal of Kyushu University. His research is focused on developing energy-\r\nefficient temperature and humidity control systems for agricultural storage, greenhouse,\r\nlivestock, and poultry applications. 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From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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\n
1. Introduction
\n
Vocalized emotional expressions such as laughter and surprise (usually accompanied by verbal interjectional utterances) often occur in daily dialogue interactions, having important social functions in human-human communication. Laughter and surprise utterances are not only simply related to funny or emotional reactions but also can express an attitude (like friendliness or interest) [1, 2].
\n
Therefore, it is important to account for such vocalized emotional/attitudinal expressions in robot-mediated communication as well. Since android robots have a highly humanlike appearance, natural communication with humans can be achieved through several types of nonverbal information, such as facial expressions and head/body gestures. There are numerous studies regarding facial expression generation in robots [3, 4, 5, 6, 7, 8, 9, 10, 11]. Most of these are related to symbolic (static) facial expression of the six traditional emotions (happy, sad, anger, disgust, fear, and surprise). However, in real daily interactions, humans can express several types of emotions and attitudes by making subtle changes in facial expression and head/body motion.
\n
When expressing an emotion, humans not only use facial expressions but also synchronize other modalities, such as head and body movements as well as vocalic expressions. Due to a high humanlike appearance in androids, the lack of a modality or of a suitable synchronization among different modalities can cause a strongly negative impression (the “uncanny valley”), when an unnatural facial expression or motion is produced. Therefore, it is important to clarify methodologies to generate motions that look natural, through appropriate timing control.
\n
The author’s research group has been working on improving human-robot communication, by implementing humanlike motions in several types of humanoid robots. So far, several methods for automatically generating lip and head motions of a humanoid robot in synchrony with the speech signal have been proposed and evaluated [12, 13, 14, 15]. Throughout the evaluation experiments, it has been observed that more natural (humanlike) behaviors by a robot are expected, as the appearance of the robot approaches the one of a human, such as in android robots. Furthermore, it has been observed that unnaturalness occurs when there is a mismatch between voice and motion, especially during short-term emotional expressions, like in laughter and surprise. To achieve a smooth human-robot interaction, it is essential that natural (humanlike) behaviors are expressed by the robot.
\n
In this chapter, motion generation for two vocalized emotional expressions, laughter and surprise, is being focused on. These are usually shorter in duration in comparison to other emotion expressions like happiness, sadness, anger, and fear, and thus it is important to account for a suitable timing control between voice and movements of facial parts, head, and body. The control of different modalities is investigated for achieving natural motion generation during laughter and surprise events of humanoid robots (i.e., when the robot produces a laughter or a vocalized surprise reaction).
\n
In Section 2, related works on motion analysis and generation during emotion expression are presented. In Section 3, the motion generation methods for laughter and surprise expressions are described, along with the motion control methods of an android robot. The motion generation methods are based on analysis results of human behaviors during dialogue interactions [16, 17]. Sections 4 and 5 present evaluation results on the effectiveness of controlling different modalities of the face, head, and upper body (eyebrow raising, eyelid widening/narrowing, lip corner/cheek raising, eye blinking, head motion and torso motion control) and different motion control levels for laughter and surprise expressions. The effects of each modality are investigated through subjective experiments using an android robot as test bed. Section 6 concludes the chapter and presents future work topics. The contents of this chapter are partially included in the author’s previously published studies [18, 19]. Readers are invited to refer to those studies, for more details on the motion analysis results.
\n
\n
\n
2. Related work
\n
As stated in the introduction, it is important to synchronize a variety of modalities, including facial movements, speech, and head/body movements, in order to suitably express an emotion.
\n
It has been reported in the emotion-recognition field that the use of both audio and visual modalities provides higher recognition rates than using a single modality [20, 21]. It is also reported that using face and head modalities in combination to the speech modality improves the expression of an emotion in CG (computer graphics) animation, in comparison to using only the face modality [22].
\n
The synchronization of speech and facial expression has also been investigated.
\n
It has been reported that the emotion perceived from the facial expression is altered, when there is a mismatch between the emotions conveyed by the voice and by facial expressions [23]. It has also been reported that when both voice and facial expressions are presented, the judgment of the perceived emotion is strongly influenced by one of the modalities, if the emotion expression of the other modality is ambiguous [24]. It has also been reported that there is a systematic link between eyebrow movements and the fundamental frequency of the voice [25].
\n
Various methods have been proposed for generating several types of facial expressions in android robots [5, 6, 7, 8, 9, 10, 11]. However, most of these methods are based on FACS (facial action coding system [26]) for positioning and controlling the actuators to reproduce humanlike facial expressions or for modeling skin deformation based on mechanical deformation models. Furthermore, there has been no evaluation of the synchronization of speech and facial expression and the face-body-head coordination, in all of these works. It is important to evaluate the effects of multimodal expression, for expressing differences of nuance in emotion rather than merely evaluating symbolic facial expressions. Previous studies indicate that the facial parts should also be moved in synchrony with the changes in speech features, in order to achieve natural motion generation. From the same perspective, head and body modalities should also be controlled in synchrony with speech.
\n
However, no previous studies have tackled the challenge of developing suitable multimodal expression control in android robots.
\n
Regarding laughter motion generation particularly, several studies have been reported in the CG animation field. Most of them are related to the ILHAIRE project [27]. For example, a model which generates facial motion position only from laughter intensity is proposed, based on the relation between laughter intensity and facial motion [28]. In [29], the laughter synthesis model above is extended by adding laughter duration as input and selecting recorded facial motion sequences from human motion data. A multimodal laughter animation synthesis method is proposed in [30], by generating lip and jaw motions from speech and pseudo-phoneme features, head and eyebrow motions from pseudo-phoneme and duration features, and torso and shoulder motions from head pitch rotation. In [31], methods to generate rhythmic body movements (torso leaning and shoulder vibration) during laughter are proposed. The torso leaning and shoulder vibrations are reconstructed from human-captured data through synthesis of two harmonics.
\n
Another issue regarding robotics application is that android robots have limitations in the motion DOF (degrees of freedom) and motion range, different from CG agents. Those studies on CG agents have assumed rich 3D models for facial motions, which cannot be directly applied to the android robot control. Therefore, it is important to clarify the effectiveness of different motion generation strategies for providing natural impressions during emotional expressions, under limited DOFs. Some studies have implemented facial expression of smiling or laughing in robots for human-robot interaction [3, 4]. However, these dealt with symbolic facial expressions, so that dynamic features and other modalities during laughter are not taken into account.
\n
In this study, the motion coordination and the effects of several modalities are taken into account for the motion generation in laughter and vocalized surprise expressions.
\n
\n
\n
3. Motion generation in laughter and surprise expressions
\n
The motion generation methods during laughter and surprise utterances are based on analysis results on human-human dialogue interaction data [16, 17, 18, 19]. The motion generation methods account for dynamic properties of a motion in synchrony with speech (i.e., when a motion starts and ends relative to the laughter/surprise expression). The main results of motion timing analyses are summarized in Section 3.1. The motion generation approaches for laughter and surprise expressions and the motion control methods in an android robot are described in Section 3.2.
\n
\n
3.1 Motion timing analysis results
\n
Analysis on laughter motion data indicates that the start time of the smiling facial expression (eye narrowing and lip corner raising) usually matches with the start time of the laughing speech, while the end time of the smiling face (i.e., the instant the face turns back to the normal face) is usually delayed relatively to the end time of the laughing speech by 1.2 ± 0.5 s. An eye blinking is usually accompanied at the instant the face turns back from the smiling face to the normal face. This was observed in 70% of the laughter events. Regarding lip corner raising, it was observed that the lip corners are clearly raised at the laughter segments by expressing a smiling face, while they are slightly raised over a longer period in non-laughing intervals by expressing a slightly smiling face. The percentage in time of smiling faces was 20%, while by including slight smiling faces the percentage in time was 81% on average, ranging from 65 to 100% (i.e., one of the speakers showed slight smiling facial expressions over the whole dialogue). Obviously, these percentages are dependent on the person and the dialogue context. In the analyzed data, most of the conversations were in joyful context. Regarding the upper-body motion, both forward and backward motions are observed. The pitch angle rotation velocities for upper-body motion were 10 ± 5°/s for forward and −10 ± 4°/s for backward directions.
\n
The main findings for the analysis on surprise motion are as follows. First, the occurrence rate of a motion during surprise utterances varies depending on whether the surprise expression is emotional/spontaneous, intentional/social, or quoted, and this rate is highly correlated to the degree of expression in emotional/spontaneous surprise. Second, different motion types have different occurrence rates according to the surprise expression degree. In particular, body backward motion appears with higher frequency when expressing high surprise degrees. Regarding motion time issues, the onset instants of face, head, and body motion are most of the time synchronized with the start time of the surprise utterances, while offset instants are usually later than the end time of the utterances, similarly to the observations in laughter motion analysis. However, the offset times were different. For eyebrow raise, the onset duration was faster than the offset duration, with averages around 200–300 ms for onset and 400–500 ms for offset. For the upper body, onset and offset durations were both around 0.8 s for small movements, and around 1.2 and 1.5 s for large movements.
\n
More details on the motion analysis results can be found in [16, 17, 18, 19], including different types and functionalities of laughter and surprise in natural dialogue interactions.
\n
\n
\n
3.2 Description of motion generation in laughter and surprise expressions and control methods in an android robot
\n
Based on the motion timing analysis results presented in Section 3.1, motion generation methods during laughter and surprise utterances are proposed, by accounting for the following modalities: facial expression control (eyelid narrowing and lip corner raising for laughter, eyelid widening and eyebrow raising for surprise), head motion control (head pitch direction), eye blinking control at the transition between smiling/surprising face to the neutral face, and body motion control (torso pitch direction).
\n
\n\nFigure 1\n shows a block diagram of the motion generation method for laughter and surprise utterances in an android robot. The method requires the speech signal and the laughing/surprise intervals as input. In autonomous robots, the laughing speech intervals and surprise utterance intervals are given a priori, while in tele-operated robots, these have to be automatically detected from the speech signal of the tele-operator.
\n
Figure 1.
Block diagram of the motion generation during laughing speech and surprise utterances.
\n
A female-type android robot, called ERICA, was used to evaluate the effects of different modalities for motion generation. However, the methodology can be applied to any robot having equivalent degrees of freedom (DOFs). \nFigure 2\n shows the external appearance and the actuators of the android robot.
\n
Figure 2.
External appearance of the female-type android robot ERICA and corresponding actuators.
\n
As shown in \nFigure 2\n, the android ERICA has 13 degrees of freedom for the face, 3 degrees of freedom for the head motion, and 2 degrees of freedom for the upper-body motion. Among these, the following ones were controlled for laughter and surprise expressions: upper eyelid control (actuator 1), lower eyelid control (actuator 5), eyebrow raise control (actuator 6), lip corner raise control (actuator 8, cheek is also raised), lip corner stretch control (actuator 10), jaw lowering (mouth opening) control (actuator 13), head pitch control (actuator 15), and upper-body pitch control (actuator 18). All actuator commands range from 0 to 255. The numbers in red in \nFigure 2\n indicate default actuator values for the neutral position.
\n
\n
3.2.1 Facial motion control
\n
Before explaining how the facial motion is controlled for different facial expressions, it is worth to clarify that the actuator values presented in this section were manually adjusted for the android ERICA, in order to achieve a desired facial expression. Thus, the actuator values are included for reference, but for other robots having different actuation ranges, these values have to be adjusted by looking at the resulting facial expressions.
\n
For the facial expression during laughter, the lip corner is raised (act[8] = 200), and the eyelids are narrowed (act[1] = 128, act[5] = 128). These values were set so that a smiling face can be clearly identified, as shown in the right panel of \nFigure 3\n (compare the generated smiling face in the right panel with the neutral face in the left panel). The mouth aperture depends on the vocalic contents of the laughing voice, as will be explained later. The timing of the facial motion control is based on the analysis results, so that the eyelid and lip corner actuator commands are sent at the instant the laughing speech interval starts, and the actuator commands are set back to the neutral position 1–1.5 s after the end of the laughing speech interval.
\n
Figure 3.
Examples of generated facial expressions by eyelid and lip corner control: neutral face (left), idle slight smile face in non-laughter intervals (middle), and smile face during laughter intervals (right).
\n
During preliminary analysis on motion generation, it has been observed that the facial expression of the neutral face (i.e., in non-laughter intervals) looked scary for the context of a joyful conversation. In fact, the lip corners were slightly or clearly raised in 80% of the dialogue intervals. A slight smile face was kept during non-laughter intervals, by controlling the eyelids and lip corner actuators to have intermediate values between the laughter smiling face and the neutral (non-expression) face. For the facial expression during the idle slight smile face, the lip corner is partially raised (act[8] = 100), and the eyelids are partially narrowed (act[1] = 90, act[5] = 80), to obtain the impression of a slight smiling face, as shown in the middle panel of \nFigure 3\n.
\n
For the facial expression in surprise utterances, the eyebrow raise and eyelid widening are coordinated and controlled at two levels of expression. The target actuator values are set by looking at the facial expressions of the android robot, in order to provide an appearance of a slight surprise face for level 1 and a clear surprise face for level 2. For the android ERICA, the target eyebrow actuators are set to act[6] = 127 for level 1 and act[6] = 255 for level 2, and the upper and lower eyelid actuators are set to {act[1] = 80; act[5] = 60} for level 1 and {act[1] = 40; act[5] = 30} for level 2. For the neutral idle face (corresponding to level 0), these actuators are set to {act[6] = 0; act[1] = 90; act[5] = 80}. As stated before, these values have to be manually adjusted for different robots, in a way to obtain the desired facial expression. \nFigure 4\n shows examples of the produced facial expressions for each of these levels. The facial expression at level 1 may not appear to be a surprised facial expression by only looking at the static picture. However, when looking at the facial movements from the neutral face, it is possible to perceive a change in the facial expression.
\n
Figure 4.
Examples of generated facial expressions for eyebrow and eyelid control at level 0 (neutral idle face, left), level 1 (slight surprise face, middle), and level 2 (clear surprise face, right).
\n
Regarding the timing of motion control, the eyelid and eyebrow actuator commands are sent at the instant the surprise utterance interval starts, and the actuator commands are set to move back to the neutral position within 0.5 s after the end of the utterance.
\n
For both laughter and surprise expressions, an eye blinking motion is added, considering that an eye blinking is usually accompanied when the facial expression turns back to the neutral face. An eye blink is implemented in the android, by closing the eyes (act[1] = 255 and act[5] = 255) during a brief period of 100 ms and opening the eyes back to the neutral face (act[1] = 64, act[5] = 0) or to an idle smiling face (act[1] = 90; act[5] = 80), as shown in the left and middle panels of \nFigure 3\n.
\n
\n
\n
3.2.2 Upper-body motion control
\n
For laughing speech, the upper body is moved to the forward and backward directions. In order to achieve smooth movements, the upper-body actuator is controlled according to half cosine functions, as defined in the following expressions:
The upper body is moved from the start point of a laughing speech interval, in order to achieve a maximum target angle corresponding to the actuation value 𝑢𝑝𝑏𝑜𝑑𝑦𝑡𝑎𝑟𝑔𝑒𝑡, in a time interval of 𝑇𝑚𝑎𝑥.
\n
From the end point of the laughing speech interval, the upper body is moved back to the neutral position according to an inverse cosine function as shown in the following expression.
𝑢𝑝𝑏𝑜𝑑𝑦𝑒𝑛𝑑 and 𝑡𝑒𝑛𝑑 are the actuator value and the time at the end point of the laughter speech interval, and 𝑢𝑝𝑏𝑜𝑑𝑦𝑛𝑒𝑢𝑡𝑟𝑎𝑙 corresponds to the actuator value for the android’s neutral pose. Thus, if the laughter interval is shorter than 𝑇𝑚𝑎𝑥, the upper body does not achieve the maximum angle.
\n
The 𝑢𝑝𝑏𝑜𝑑𝑦𝑡𝑎𝑟𝑔𝑒𝑡 was adjusted to −10 degrees (which is the mean body pitch angle range in human data), and the time interval 𝑇𝑚𝑎𝑥 to achieve the maximum angle was adjusted to 1.5 s (a bit longer than the human average time, to avoid jerky motion in the android).
\n
For surprise utterances, the upper body is moved in the backward direction at the start point of the surprise utterance and then moved back to the neutral position. Two levels are controlled corresponding to about 2 degrees for level 1 and 4 degrees for level 2 (which was the maximum angle achieved by the android).
\n
Regarding the timing control, the upper body is moved back to the neutral position from 0.3 s after the end point of the surprise utterance interval. The onset duration to achieve the maximum angle is set to 0.8 s, while the offset duration to move back to the neutral idle position is set to 1.5 s. Half cosine functions are used to smooth motion velocity changes in the current and target positions, as in the expressions (1) and (2) for laughter motion control.
\n
The torso pitch actuator in the android (actuator 18) is then controlled around the neutral pose actuator value (𝑢𝑝𝑏𝑜𝑑𝑦𝑛𝑒𝑢𝑡𝑟𝑎𝑙), according to the following expression:
For the head motion control, a method for controlling the head pitch (vertical movements) from the voice pitch (fundamental frequencies, F0) is employed. This is based on the fact that there is some correlation between head motion and voice pitch [15, 32]. Although this correlation is not very high (i.e., this control strategy is not exactly what humans do during speech), natural head motions are expected to be generated during laughing and surprise expressions, since humans usually tend to raise the head for high F0s especially in inhaling laughter intervals, and high-pitched surprise utterances. The following expression is used to convert F0 values to the head pitch actuator:
where center_F0 is the speaker’s average F0 value (around 120 Hz for male and around 240 Hz for female speakers) converted to semitone units and F0 is the current.
\n
\nF0 value (in semitones) and F0_scale is a scale factor for mapping the F0 (voice pitch) changes to head pitch movements. For the experiments, F0_scale is set in a way that a 1-semitone change in voice pitch corresponds to ~1-degree change in head pitch rotation.
\n
Preliminary evaluation has shown that the robot motion looked unnatural during a surprise expression, when the head was facing the upward direction, while the body moved in the backward direction. In fact, it has been observed from the human motion data that the speaker is usually looking at the dialogue partner during a surprise expression. The following additional control in the head pitch actuator deals with this issue, by moving the head in the inverse direction to the body pitch movement:
The head pitch actuator in the android (actuator 15) is then controlled around the neutral pose actuator value (headpitchneutral\n), according to the following expression:
The lip motion is controlled based on a formant-based lip motion control method [12]. The method is based on the fact the first and second formants (resonance frequencies in the vocal tract) can be associated to the lip height and lip width, respectively, after some speaker normalization procedure. The jaw actuator (actuator 13) is controlled using the estimated lip heights, and the lip stretch actuator (actuator 10) is controlled using the estimated lip widths.
\n
In this way, appropriate lip shapes can be generated in laughter segments with different vowel qualities (such as in “hahaha” and “huhuhu”) as well as in vocalized surprise segments with different vowel qualities (such as in “eh!” and “ah!”), since the method is based on the vowel formants.
\n
\n
\n
\n
\n
4. Evaluation of the laughter motion generation
\n
This section presents evaluation results on the laughter motion generation method, by controlling different modalities of the face, head, and body. The experimental setup is described in Section 4.1; the evaluation results and the interpretation of the results are presented in Section 4.2.
\n
\n
4.1 Experimental setup
\n
Two conversation passages of about 30 s including multiple laughter events were extracted from a dialogue database, and the corresponding motion data was generated in the android ERICA, based on the method described in the Section 3.2. The speech signal and the laughter speech interval information are provided as input. The two conversation passages were extracted from different speakers and will be named “voice 1” and “voice 2.” “voice 1” includes social and embarrassed laughter, while “voice 2” includes emotional and funny laughter.
\n
\n\nTable 1\n shows the five motion types (named “A”–“E”) generated in the android, taking the effects of different modalities into account.
\n
\n
\n
\n\n
\n
Motion
\n
Controlled modalities
\n
\n\n\n
\n
A
\n
Face (eyelids + lip corners) + eye blink + head
\n
\n
\n
B
\n
Face (eyelids + lip corners) + head
\n
\n
\n
C
\n
Face (eyelids + lip corners) + eye blink
\n
\n
\n
D
\n
Face (eyelids + lip corners) + eye blink + head + idle smiling face
\n
\n
\n
E
\n
Face (eyelids + lip corners) + eye blink + head + idle smiling face + upper body
\n
\n\n
Table 1.
The controlled modalities for generating five motion types during laughter events.
\n
“Eyelids” and “lip corners” are controlled to express a smiling facial expression (corresponding to Duchenne smile faces [33]) during laughter. These are present in all conditions. “Lip corners” corresponds to a lip corner raising motion, which is also accompanied by a cheek raising motion in the android, while “eyelids” corresponds to an eye narrowing motion.
\n
“Eye blink” corresponds to an eye blinking motion, when the face expression is turned back to the neutral (idle) face, from a smiling face. “Head” corresponds to the motion control of the head pitch (vertical head movements) from the voice pitch. “Idle smile face” corresponds to a slight smiling face during non-laughter intervals. “Upper body” corresponds to the motion control of the torso pitch (front-back upper-body movements) in long laughter events.
\n
Video clips are recorded for each motion type and used in the subjective evaluation experiments. Video-based evaluation is conducted instead of face-to-face evaluation since the participants do not interact with the robot. Pairwise comparisons are conducted in order to investigate the effects of the different motion controls. The evaluated motion pairs are described in \nTable 2\n.
\n
\n
\n
\n\n
\n
Motion pair
\n
Differences in the controlled modalities
\n
\n\n\n
\n
A vs. B
\n
Presence/absence of “eye blink” control (“eyelids,” “lip corners,” and “head” are in common)
\n
\n
\n
A vs. C
\n
Presence/absence of “head” control (“eyelids,” “lip corners,” and “eye blink” are in common)
\n
\n
\n
A vs. D
\n
Absence/presence of “idle smiling face” control (“eyelids,” “lip corners,” “eye blink” and “head” are in common)
\n
\n
\n
D vs. E
\n
Absence/presence of “upper-body” control (“eyelids,” “lip corners,” “eye blink,” “head,” and “slightly smiling face” are in common)
\n
\n\n
Table 2.
Motion pairs for comparison of the effects of different modalities in laughter.
\n
In the evaluation experiments, pairs of videos are presented for the participants. The order of the videos for each pair is randomized. The videos are allowed to be replayed at most two times each.
\n
After watching each pair of videos, participants are asked to grade the preference scores for pairwise comparison, and the overall naturalness scores for the individual motions, in 7-point scales, according to the questionnaire below. The numbers within parenthesis are used to quantify the perceptual scores.
\n
Q1. Which motion looked more natural (humanlike)? Motion A is clearly more natural (−3), Motion A is more natural (−2), Motion A is slightly more natural (−1), Difficult to decide (0), Motion B is slightly more natural (1), Motion B is more natural (2), Motion B is clearly more natural (3).
\n
Q2. Is the motion natural (humanlike)? very unnatural (−3), unnatural (−2), slightly unnatural (−1), difficult to decide (0), slightly natural (1), natural (2), very natural (3).
\n
The first question was answered for each video pair, while the second question was answered for each of the individual videos. For the motion types A and D, which appear multiple times, individual scores are graded only once, at the first time the videos are seen. Besides the perceptual scores, participants are also asked to write the reason of their judgments, if a motion is perceived as unnatural.
\n
The sequence of motion pairs above was evaluated for each of the conversation passages (“voice 1” and “voice 2”). Twelve remunerated subjects (male and female, aged from 20 to 40 s) participated in the evaluation experiments.
\n
\n
\n
4.2 Evaluation results
\n
\n\nFigure 5\n shows the evaluation results for pairwise comparisons. Statistical analyses are conducted by t-tests (* for p < 0.05 and ** for p < 0.01 confidences). For the preference scores in the pairwise comparison, significance tests are conducted in comparison to 0 scores, which correspond to unperceivable differences.
\n
Figure 5.
Subjective preference scores between motion pairs in laughter motion generation (average scores and standard deviations). (Negative average scores indicate the first condition was preferred, while positive average scores indicate that the second condition was preferred).
\n
The differences between the motion types A and B (with and without eye blinking control) are subtle, so that most of the participants could not perceive differences. However, subjective scores showed that the inclusion of eye blinking control was evaluated to look more natural for both conversation passages (“voice 1” and “voice 2”).
\n
The comparison between the motion types A and C (with and without head motion control) indicates that the inclusion of head motion control clearly increases the motion naturalness (p < 0.01) for both “voice 1” and “voice 2.” The participants’ judgments were remarkable (the differences in the motion videos were clear).
\n
The comparison between the motion types A and D (without or with idle smile face) indicates that keeping a slight smiling face in the intervals other than laughing speech was also effective to increase motion naturalness (p < 0.01).
\n
Finally, the comparison between the motion types D and E (with and without upper-body motion) indicates that the inclusion of upper-body motion also increases motion naturalness (p < 0.05 for “voice 1,” p < 0.01 for “voice 2”). The differences are more evident in “voice 2” (in comparison to “voice 1”) since “voice 2” contained longer duration for the laughter events within the conversation passage, and consequently the upper-body movements were more clear.
\n
\n\nFigure 6\n shows the results for perceived naturalness graded for each motion type. The results of subjective scores shown in \nFigure 6\n indicate that, overall, slightly natural to natural motions could be achieved by the laughter motion generation method including all motion control types.
\n
Figure 6.
Subjective naturalness scores for each motion type in laughter motion generation (average scores and standard deviations).
\n
The motion type C is the only one that received negative average scores, meaning that if the head does not move, the laughter motions will look unnatural. This indicates that the F0-based method for head pitch control is effective for increasing motion naturalness during laughter. However, some of the participants pointed out that the motions would look more natural, if other axes of the head also move. This is a topic for future work.
\n
Regarding the insertion of eye blinking, at the instant the facial expression turns back to the neutral face (motion type B), although the comparisons between motion types A and B were not statistically significant (since the visual difference is subtle). However, the participants who perceived the difference judged the presence of eye blinking to be more natural. The eye blinking control is thought to work as a cushion to alleviate the unnaturalness caused by sudden changes in facial expression. The insertion of such a small motion could possibly be used as a general method for other facial expressions.
\n
The control of idle slight smile face in non-laughter intervals (motion type D) was shown to be effective to improve the naturalness, since the conversation context was in joyful situations. However, for a more appropriate control of slight smile face, detection of the situation might be important.
\n
The reason why motion type E (with upper-body motion) was clearly judged as more natural than motion type D (without upper-body motion) for “voice 2” is that it looks unnatural if the upper body does not move during long and strong emotional laughter. The proposed upper-body motion control was effective to relieve such unnaturalness. Regarding intensity of the laughter, although it was implicitly accounted in the present work, by assuming high correlation between pitch and duration with intensity, it could also be explicitly modeled on the generated motions.
\n
\n
\n
\n
5. Evaluation of the surprise motion generation
\n
This section presents evaluation results on the surprise motion generation method, by controlling different modalities and different control levels of the face, head, and body. The experimental setup is described in Section 5.1, and the evaluation results are presented and discussed in Section 5.2.
\n
\n
5.1 Experimental setup
\n
The surprise motion generation method was evaluated for the interjectional utterances “e” and “a,” which are the ones that most frequently occurred in dialogue interactions for expressing surprise. Sixteen dialogue passages of about 10 s including interjectional utterances “e” or “a” expressing different degrees of surprise were extracted from the dialogue database. Then motion was generated in the android, based on the method described in Section 3.2. The speech signal and the surprise utterance interval information are provided as input for motion generation.
\n
\n\nTable 3\n lists the six motion types generated in the android, for evaluating the effects of different modalities and different degrees of motion control, during surprise expressions. The motion types in \nTable 3\n are named according to the modality and control levels: “e” stands for eyebrow and eyelids, “h” for head, and “b” for body. The numbers following these letters indicate the control levels. Level “0” indicates no control, level “1” indicates small movements, and level “2” indicates large movements. The facial expressions of levels “1” and “2” for “eyebrows + eyelids” are shown in the middle and right panels in \nFigure 4\n. The levels “1” and “2” for body motion indicate maximum range of 2 and 4 degrees, respectively, as explained in Section 3.2. The head movements are controlled from the voice, so that 1-semitone change in voice pitch corresponds to ~1 degree for head pitch (Section 3.2). The six motion types were chosen in order to reduce the efforts of the annotators while allowing the comparison of pairs between presence/absence and degree of a motion.
\n
\n
\n
\n\n
\n
Motion type
\n
Controlled modalities
\n
\n\n\n
\n
e2 + h0 + b0
\n
Eyebrows + eyelids (level 2)
\n
\n
\n
e2 + h0 + b2
\n
Eyebrows + eyelids (level 2) + body (level 2)
\n
\n
\n
e1 + h1 + b0
\n
Eyebrows + eyelids (level 1) + head
\n
\n
\n
e2 + h1 + b0
\n
Eyebrows + eyelids (level 2) + head
\n
\n
\n
e2 + h1 + b1
\n
Eyebrows + eyelids (level 2) + head + body (level 1)
\n
\n
\n
e2 + h1 + b2
\n
Eyebrows + eyelids (level 2) + head + body (level 2)
\n
\n\n
Table 3.
Modalities controlled for generating six motion types in surprise utterances.
\n
Video clips were recorded, for each motion type and each dialogue passage, to be used in the subjective experiments.
\n
Considering that the range and amount of body movements will be small in short interjectional utterances (around 200 ms), only the three motion types without body control (e2 + h0 + b0, e1 + h1 + b0, and e2 + h1 + b0) were evaluated for short interjectional utterances. For the long interjectional utterances, all six motion types were evaluated. From the eight “a” utterances, seven were short, while from the eight “e” utterances, four were short. Thus, a total of 63 videos ((7 + 4) × 3 short utterances + (1 + 4) × 6 long utterances) were used for evaluation.
\n
In the experiments, the participants are asked to watch all 63 videos and to grade each video with perceptual subjective scores, according to the questionnaire below. The numbers within the parentheses were used to quantify the perceptual scores. The order of the videos is randomized, and the participants are allowed to watch at most two times each.
\n
Q1. What is the perceived degree of surprise expression (regardless of whether an expression is emotional/spontaneous or social/intentional)? No expression (0), slight expression (1), clear expression (2), strong expression (3).
\n
Q2. Is the motion natural (humanlike)? Very unnatural (−3), unnatural (−2), slightly unnatural (−1), difficult to decide (0), slightly natural (1), natural (2), very natural (3).
\n
Q3. Do you feel that the surprise expression is emotional/spontaneous or social/intentional? Intentional (−2), slightly intentional (−1), difficult to decide (0), slightly emotional (1), emotional (2).
\n
Eighteen remunerated subjects (male and female, aged from 20s to 40s) participated in the evaluation experiments.
\n
\n
\n
5.2 Evaluation results
\n
We consider that the degree of surprise expression is affected by both audio and visual modalities. In order to account for the effects of the voice modality, the utterances used in the experiment were categorized into three levels, according to their perceptual degrees of surprise graded only from the voice. The resulting number of utterances was 8 for voice group 1 (all short interjections), 7 for voice group 2 (3 short and 4 long interjections), and 1 for voice group 3 (long interjection).
\n
\n\nFigure 7\n shows the average subjective scores (vertical axes) for surprise expression degree, motion naturalness, and emotional/intentional impression, according to the voice groups (horizontal axes: surprise expression degrees by voice only), for each of the six motion types. Note that the different levels in the horizontal axis are based on voice only, while the subjective scores in the vertical axes are based on voice plus motion modalities.
\n
Figure 7.
Subjective perceptual scores of surprise expression degree (top), naturalness degree (mid), and emotional/intentional impression degree (bottom) for each motion type, according to the voice groups (horizontal axis: voice-based surprise expression degrees).
\n
Pairwise comparisons are conducted to investigate the effects of presence/absence or degree of motion control, and statistical significance tests are conducted through t-tests. Firstly, the effects of controlling the motion degrees of eyebrow and eyelids are analyzed by comparing motion types e1 + h1 + b0 and e2 + h1 + b0. It can be observed in the upper panel of \nFigure 7\n that the average perceptual scores for surprise expression degree increase by about 0.7 points (on a 0–3-point scale) for voice group 1 (p < 0.01) and by about 0.5 points in voice group 3 (p < 0.01). This indicates that a slight change in the eyebrow/eyelid control is effective for changing the perceived degree of surprise.
\n
Next, the effects of controlling the head motion modality are analyzed by comparing the results for the motion types e2 + h0 + b0 and e2 + h1 + b0. The differences in surprise expression degree between these two motion types are about 0.2 points for voice group 1 (p < 0.01) and about 0.4 points for voice group 3 (n.s., p = 0.09), which are slightly smaller than the effects of eyebrow/eyelid control.
\n
The effects of controlling the body motion modality are analyzed by comparing the results between the motion types e2 + h0 + b0 and e2 + h0 + b2 (when head motion is not controlled) or between the motion types e2 + h1 + b0 and e2 + h1 + b2 (when head motion is controlled). It is observed that, when head motion is not controlled (h0), the effects of controlling or not the body motion (b0 vs. b2) increase the surprise degrees by about 0.4–0.5 points for voice groups 2 and 3 (p < 0.01). When head motion is controlled (h1), the increase in the perceptual surprise degree is smaller by about 0.3 points (h1 + b0 vs. h1 + b2; p < 0.05), probably because the contribution of head motion is superimposed. Although the differences were not statistically significant, a gradual increase can be observed for the gradual control of body motion (b0 vs. b1 vs. b2, for the motion type e2 + h1).
\n
Regarding the naturalness scores, the results in the middle panel of \nFigure 7\n indicate slightly natural to natural scores in almost all motion types. By comparing the motion types e2 + h0 + b0 and e2 + h1 + b0, it can be inferred that head motion has important effects on the naturalness (humanlike) perception when the body does not move (b0). The naturalness scores are increased by about 0.5 points on average (p < 0.01), by inclusion of head motion.
\n
Regarding the subjective spontaneity degree, the results in the bottom panel of \nFigure 7\n show that the average scores in motion types e1 + h1 + b0 and e2 + h0 + b0 are negative in voice group 3, indicating that if the amount of motion decreases, the surprise utterances might be perceived as intentional rather than emotional (p < 0.01). On the other hand, the naturalness scores decrease in motion types with fewer motion (e1 + h1 + b0 and e2 + h0 + b0) in voice group 3 (high surprise expression degree by voice only), as shown in the middle panel of \nFigure 7\n. This is thought to be due to the mismatch between surprise expressions by voice and motion modalities. Another interpretation is that these motion types are perceived as being unnatural, because from the dialogue context, an emotional/spontaneous expression was expected.
\n
Finally, regarding the effects of the voice modality, the results in the upper panel of \nFigure 7\n for the subjective surprise degrees clearly show that within a motion type, the subjective surprise degrees increase according to the voice groups (voice-based surprise degrees). This means that the perception of surprise degree is dependent on the surprise expression from the voice and, moreover, that by controlling the motion degrees of different modalities, the degree of surprise expression transmitted from the combination of voice and motion can be biased by a certain amount. For example, for the utterances in voice group 1, the subjective surprise degree can be raised to 1.8 on average by controlling the head and eyebrows (e2 + h1 + b0), while the utterances in voice group 3 can have their subjective surprise degree reduced to around 2 if the head and body are not controlled (e2 + h0 + b0).
\n
\n
\n
\n
6. Conclusion and final remarks
\n
Methods for motion generation synchronized with laughter speech and vocalized surprise expressions were described, based on analysis results of human behaviors on facial, head, and body motions during dialogue interactions.
\n
The effectiveness of controlling different modalities of the face, head, and upper body (eyebrow raising, eyelid widening/narrowing, lip corner/cheek raising, eye blinking, head pitch, and torso pitch motion control) and different motion control levels were evaluated using an android robot. The evaluation was conducted through subjective experiments, by comparing motions generated with different modalities and different motion control levels.
\n
Evaluation results for laughter motion generation indicated that motion is perceived as unnatural, if only the facial expression (lip corner raising and eyelid narrowing) is controlled (without head and body motion control). The motion naturalness scores increased when head pitch, eye blinking (at the instant the facial expression turns back to neutral face), idle smile face (during non-laughter intervals), and upper-body motion are also controlled. The best naturalness scores are achieved when all modalities are controlled.
\n
Evaluation results for surprise motion generation indicated that (1) eyebrow/eyelid motion control is effective in changing the perceptual degrees of surprise expression, (2) upper-body motion control is effective for increasing the degrees of surprise expression and naturalness, (3) head motion is more effective for increasing naturalness (rather than surprise degree), (4) the degrees of surprise expression for different motion types are biased by the surprise degrees expressed by the voice-only modality, and (5) utterances with high surprise degrees may be interpreted as intentional (rather than emotional or spontaneous) if they are not accompanied by upper-body motion.
\n
In the present study, it was shown that with a limited number of DOFs (lip corner, eyelids, eyebrows, head pitch, torso pitch), natural motion could be generated for laughter and surprise expressions. Although the android robot ERICA is used as a test bed for evaluation, the described motion generation approach can be generalized for any robot having equivalent DOFs.
\n
Remaining topics for future work include automatic detection of laughing speech intervals and surprise utterance intervals from acoustic features, in order to automate the motion generation process from the input speech signal. Prediction of surprise expression degrees from acoustic features and explicit modeling of laughter intensity are also remaining tasks for motion generation automation. The control strategy of head tilt and shake axes, the investigation of eye blinking insertion for alleviating unnaturalness caused by sudden changes in other facial expressions, and the detection of situation for slight smile face control are remaining topics for improving motion naturalness.
\n
\n
Acknowledgments
\n
This research was supported by JST/ERATO, Grant Number JPMJER1401. Special thanks go to Takashi Minato for the contributions in the android motion control and discussions on motion generation. The author also thanks Mika Morita, Megumi Taniguchi, Kyoko Nakanishi, and Tomo Funayama for their contributions in the data analysis and experimental setup.
\n
\n',keywords:"emotion expression, laughter, surprise, motion generation, human-robot interaction, nonverbal information",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/68533.pdf",chapterXML:"https://mts.intechopen.com/source/xml/68533.xml",downloadPdfUrl:"/chapter/pdf-download/68533",previewPdfUrl:"/chapter/pdf-preview/68533",totalDownloads:722,totalViews:0,totalCrossrefCites:0,totalDimensionsCites:0,totalAltmetricsMentions:0,introChapter:null,impactScore:0,impactScorePercentile:42,impactScoreQuartile:2,hasAltmetrics:0,dateSubmitted:"February 13th 2019",dateReviewed:"July 8th 2019",datePrePublished:"August 19th 2019",datePublished:"March 25th 2020",dateFinished:"August 8th 2019",readingETA:"0",abstract:"Vocalized emotional expressions such as laughter and surprise often occur in natural dialogue interactions and are important factors to be considered in order to achieve smooth robot-mediated communication. Miscommunication may be caused if there is a mismatch between audio and visual modalities, especially in android robots, which have a highly humanlike appearance. In this chapter, motion generation methods are introduced for laughter and vocalized surprise events, based on analysis results of human behaviors during dialogue interactions. The effectiveness of controlling different modalities of the face, head, and upper body (eyebrow raising, eyelid widening/narrowing, lip corner/cheek raising, eye blinking, head motion, and torso motion control) and different motion control levels are evaluated using an android robot. Subjective experiments indicate the importance of each modality in the perception of motion naturalness (humanlikeness) and the degree of emotional expression.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/68533",risUrl:"/chapter/ris/68533",book:{id:"6865",slug:"becoming-human-with-humanoid-from-physical-interaction-to-social-intelligence"},signatures:"Carlos T. Ishi",authors:[{id:"130410",title:"Ph.D.",name:"Carlos",middleName:"Toshinori",surname:"Ishi",fullName:"Carlos Ishi",slug:"carlos-ishi",email:"carlos@atr.jp",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Advanced Telecommunications Research Institute International",institutionURL:null,country:{name:"Japan"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Related work",level:"1"},{id:"sec_3",title:"3. Motion generation in laughter and surprise expressions",level:"1"},{id:"sec_3_2",title:"3.1 Motion timing analysis results",level:"2"},{id:"sec_4_2",title:"3.2 Description of motion generation in laughter and surprise expressions and control methods in an android robot",level:"2"},{id:"sec_4_3",title:"3.2.1 Facial motion control",level:"3"},{id:"sec_5_3",title:"3.2.2 Upper-body motion control",level:"3"},{id:"sec_6_3",title:"3.2.3 Head motion control",level:"3"},{id:"sec_7_3",title:"3.2.4 Lip motion control",level:"3"},{id:"sec_10",title:"4. Evaluation of the laughter motion generation",level:"1"},{id:"sec_10_2",title:"4.1 Experimental setup",level:"2"},{id:"sec_11_2",title:"4.2 Evaluation results",level:"2"},{id:"sec_13",title:"5. Evaluation of the surprise motion generation",level:"1"},{id:"sec_13_2",title:"5.1 Experimental setup",level:"2"},{id:"sec_14_2",title:"5.2 Evaluation results",level:"2"},{id:"sec_16",title:"6. Conclusion and final remarks",level:"1"},{id:"sec_17",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'\nDevillers L, Vidrascu L. Positive and negative emotional states behind the laughs in spontaneous spoken dialogs. In: Proc. of Interdisciplinary Workshop on the Phonetics of Laughter; 2007. pp. 37-40\n'},{id:"B2",body:'\nCampbell N. Whom we laugh with affects how we laugh. In: Proc. of Interdisciplinary Workshop on The Phonetics of Laughter; 2007. pp. 61-65\n'},{id:"B3",body:'\nBreazeal C. Emotion and sociable humanoid robots. International Journal of Human-Computer Studies. 2003;59:119-155\n'},{id:"B4",body:'\nZecca M, Endo N, Momoki S, Itoh K, Takanishi A. Design of the humanoid robot KOBIAN-preliminary analysis of facial and whole body emotion expression capabilities. In: Proc. of the 8th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2008); 2008. pp. 487-492\n'},{id:"B5",body:'\nWu Y, Thalmann NM, Thalmann D, Dynamic Wrinkle A. Model in facial animation and skin aging. Journal of Visualization and Computer Animation. 1995;6(4):195-205\n'},{id:"B6",body:'\nHashimoto T, Hiramatsu S, Tsuji T, Kobayashi H. Development of the face robot {SAYA} for rich facial expressions. In: Proceedings of the SICE-ICASE International Joint Conference; 2006. pp. 5423-5428\n'},{id:"B7",body:'\nLee D, Lee T, So B, Choi M, Shin E, Yang K, et al. Development of an android for emotional expression and human interaction. In: Proceedings of the 17th World Congress the International Federation of Automatic Control; 2008. pp. 4336-4337\n'},{id:"B8",body:'\nMazzei D, Lazzeri N, Hanson D, de Rossi D. HEFES an hybrid engine for facial expressions synthesis to control human-like androids and avatars. In: Proc. the 4th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics; 2012. pp. 95-200\n'},{id:"B9",body:'\nTadesse Y, Priya S. Graphical facial expression analysis and design method an approach to determine humanoid skin deformation. Journal of Mechanisms and Robotics. 2012;4(2):021010\n'},{id:"B10",body:'\nAhn H, Lee D, Choi D, Lee D, Hur M, Lee H, et al. Designing of android head system by applying facial muscle mechanism of humans. In: Proceedings of IEEE-RAS International Conference on Humanoid Robots; 2012. pp. 799-804\n'},{id:"B11",body:'\nLoza D, Marcos S, Zalama E, Garcia-Bermejo JG, Gonzalez JL. Application of the FACS in the design and construction of a mechatronic head with realistic appearance. Journal of Physicla Agents. 2013;7(1):31-38\n'},{id:"B12",body:'\nIshi C, Liu C, Ishiguro H, Hagita N. Evaluation of formant-based lip motion generation in tele-operated humanoid robots. In: Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2012); 2012. pp. 2377-2382\n'},{id:"B13",body:'\nIshi CT, Liu C, Ishiguro H, Hagita N. Head motion during dialogue speech and nod timing control in humanoid robots. In: Proc. of 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI 2010); 2010. pp. 293-300\n'},{id:"B14",body:'\nLiu C, Ishi C, Ishiguro H, Hagita N. Generation of nodding, head tilting and gazing for human-robot speech interaction. International Journal of Humanoid Robotics (IJHR). 2013;10(1):1-19\n'},{id:"B15",body:'\nSakai K, Minato T, Ishi CT, Ishiguro H. Novel speech motion generation by modelling dynamics of human speech production. Frontiers in Robotics and AI. 2017;4(49):14\n'},{id:"B16",body:'\nIshi C, Hatano H, Ishiguro H. Audiovisual analysis of relations between laughter types and laughter motions. In: Proc. of the 8th International Conference on Speech Prosody (Speech Prosody 2016); 2016. pp. 806-810\n'},{id:"B17",body:'\nIshi C, Minato T, Ishiguro H. Motion analysis in vocalized surprise expressions. In: Proc. Interspeech 2017; 2017. pp. 874-878\n'},{id:"B18",body:'\nIshi CT, Minato T, Ishiguro H. Motion analysis in vocalized surprise expressions and motion generation in android robots. IEEE Robotics and Automation Letters. 2017;2(3):1748-1754\n'},{id:"B19",body:'\nIshi CT, Minato T, Ishiguro H. Analysis and generation of laughter motions, and evaluation in an android robot. APSIPA Transactions on Signal and Information Processing. 2019;8(e6):1-10\n'},{id:"B20",body:'\nBusso C, Deng Z, Yildirim S, Bulut M, Min Lee C, Kazemzadeh A, et al. Analysis of emotion recognition using facial expressions, speech and multimodal information. In: Proceedings of the 6th International Conference on Multimodal Interfaces; 2004. pp. 205-211\n'},{id:"B21",body:'\nAlonso-Mart F, Malfaz M, Sequeira J, Gorostiza JF, Salichs MA. A multimodal emotion detection system during human-robot interaction. Sensors. 2013;13(11):15549-15581\n'},{id:"B22",body:'\nUz B, Gudukbay U, Ozguc B. Realistic speech animation of synthetic faces. In: Proceedings of the Computer Animation; 1998. pp. 111-118\n'},{id:"B23",body:'\nAdams A, Mahmoud M, Baltrusaitis T, Robinso P. Decoupling facial expressions and head motions in complex emotions. In: Proceedings of the 2015 International Conference on Affective Computing and Intelligent Interaction; 2015. pp. 274-280\n'},{id:"B24",body:'\nMassaro DW, Egan PB. Perceiving affect from the voice and the face. Psychonomic Bulletin & Review. 1996;3(2):215-221\n'},{id:"B25",body:'\nCave C, Guaitella I, Bertrand R, Santi S, Harlay F, Espesser R. About the relationship between eyebrow movements and F0 variations. In: Proceedings of the 4th International Conference on Spoken Language Processing; 1996. pp. 2175-2179\n'},{id:"B26",body:'\nEkman P, Friesen WV. Head and body cues in the judgment of emotion: A reformulation. Perceptual and Motor Skills. 1967;24(3):711-724\n'},{id:"B27",body:'\nThe ILHAIRE project (Incorporating laughter into human avatar interactions: Research and experiments). Available from: http://www.ilhaire.eu/\n\n'},{id:"B28",body:'\nNiewiadomski R, Pelachaud C. Towards multimodal expression of laughter. In: IVA; 2012. pp. 231-244\n'},{id:"B29",body:'\nNiewiadomski R, Hofmann J, Urbain J, Platt T, Wagner J, Piot B, et al. Laugh-aware virtual agent and its impact on user amusement. In: AAMAS; 2013. pp. 619-626\n'},{id:"B30",body:'\nDing Y, Prepin K, Huang J, Pelachaud C, Artieres T. Laughter animation synthesis. In: Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems; 2014. pp. 773-780\n'},{id:"B31",body:'\nNiewiadomski R, Mancini M, Ding Y, Pelachaud C, Volpe G. Rhythmic body movements of laughter. In: Proc. of 16th International Conference on Multimodal Interaction; 2014. pp. 299-306\n'},{id:"B32",body:'\nYehia HC, Kuratate T, Vatikiotis-Bateson E. Linking facial animation, head motion and speech acoustics. Journal of Phonetics. 2002;30:555-568\n'},{id:"B33",body:'\nEkman P, Davidson RJ, Friesen WV. The Duchenne smile: Emotional expression and brain physiology II. Journal of Personality and Social Psychology. 1990;58(2):342-353\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Carlos T. Ishi",address:"carlos@atr.jp",affiliation:'
ATR Hiroshi Ishiguro Labs., Kyoto, Japan
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1. Introduction
In the recent years, machine learning techniques have been utilized to solve problems at hand across multitudes of industries and topics. In the healthcare industry, these techniques are often applied to a variety of healthcare claims and electronic health records data to garner valuable insights into diagnostic and treatment pathways in order to help optimize patient healthcare access and treatment process [1]. Unfortunately, many of these applications resulted in inaccurate or irrelevant research results, as proper research protocols were not fully followed [2]. On the other hand, statistics has been the basis of analysis in healthcare research for decades, especially, in the areas of clinical trials and health economics and outcomes research (HEOR), where the precision and accuracy of analyses have been the primary objectives [3]. Furthermore, the classical statistics methodologies are often preferred in those research areas to ensure the ability to replicate and defend the results and ultimately, the ability to publish the research content in peer-reviewed medical journals [3]. The increased availability of data, including data from wearables, provided the opportunity to apply a variety of analytical techniques and methodologies to identify patterns, often hidden, that could help with optimization of healthcare access as well as diagnostic and treatment process [4].
With the rapid increase in data from the healthcare and many other industries, it is important to consider how to select well - suited statistical and machine learning methodologies that would be best for the problem at hand, the available data type, and the overall research objectives [5]. Machine learning alone or complemented by statistical modeling is becoming, not just a more common, but a desired convergence to take advantage of the best of both approaches for advancing healthcare outcomes [1]. Please note that this book chapter was originally posted on the Cornell University’s research working article website: https://arxiv.org. The book chapter content is mostly the same between the two versions [6].
2. Machine learning foundation is in statistical learning theory
Machine learning (ML) is considered a branch of artificial intelligence and computer science that focuses on mimicking human behaviors through a set of algorithms and methods that use historical values to predict new values [7], without specifically being coded to do so and thereby learning over time [8, 9]. ML is grounded in statistical learning theory (SLT), which provides the constructs used to create prediction functions from data. One of the first examples of SLT was the creation of the support vector machine (SVM), the supervised learning method that can be used as for both classification and regression and has become a standard in modeling how to recognize visual objects [7]. SLT formalizes the model that makes a prediction based on observations (i.e., data) and ML automates the modeling [7].
SLT sets the mathematical and theoretical framework for ML as well as the properties of learning algorithms [7] with the goals of providing mechanisms for studying inference and creating algorithms that become more precise and improved over time [8]. SLT is based multivariate statistics and functional analysis [8]. Functional analysis is the branch of statistics that measures shapes, curves, and surfaces, extending multivariate vector statistics to continuous functions and finding functions that describe data patterns [8]. Inductive inference is the process of generalizing and modeling past observations to make predictions for the future; SLT formalizes the modeling concepts of inductive inference, while ML automates them [8].
For example, pattern recognition is considered a problem of inductive inference and SLT, as it is a curving-fitting problem, and one of the most common applications of ML [7, 8, 9]. Pattern recognition is not suited for traditional computer programming as the inferences needed are not free of assumptions and the patterns are not easily described or labeled programmatically with deterministic functions. The standard mathematics behind SLT makes no assumptions on distributions, uses stochastic functions that can include humans labeling the “right” classification, i.e., training data, and can assume that the probability of the occurrence of one observation is independent of another thereby including the concept of randomness [7, 8, 9]. These tenets are therefore those of ML as well.
SLT also provides the definition of terms often using in ML such as overfitting, underfitting and generalization. Overfitting is when the presence of noise in the data negatively affects training and the ultimate model performance because the noise is being incorporated into the learning process, thereby giving error when the model sees new data [8, 9]. Underfitting is when the noise impacts both performance on training data as well as new and unseen data [9]. In ML, discussion about underfitting and overfitting are often used to describe models that do not generalize the data effectively and might not present the right set of data elements to explain the data patterns and posited hypotheses [9]. Underfitting is often defined when model which is missing features that would be present in the most optimized model, akin to a regression model not fully explaining all of the variance of the dependent variable [9]. In a similar vein, overfitting is when the model contains more features or different features than is optimal, like a regression model with autocorrelation or multicollinearity [9].
The general goal of learning algorithms and therefore ML model optimization is to reduce the dimensions, features, or data variables to the fewest number needed as that reduces noise or the impact of trivial variables that can overfit or unfit [8, 9]. A regularization model can then become generalized to perform not just on the past or the training data, but also on future and yet unseen data [8, 9]. Although true generalization needs both the right modeling criteria as well as strong subject matter knowledge [8].
Often dimension reduction approaches like Principal Component Analysis (PCA) or boot strapping techniques used along with subject matter expertise can help resolve how to refine models, combat fit challenges, as well as improve generalization potential [9, 10]. Furthermore, understanding the studied population and data characteristics can further help define the data to be used, variable selection, and proper model set up [10].
3. Similarities between machine learning and statistical modeling
Statistical modeling is based on SLT and use of mathematical models and statistical assumptions to generate sample data and make predictions about the real world occurrences. A statistical model is often represented as a collection of probability distributions on a set of all possible outcomes. Furthermore, statistical modeling has evolved in the last few decades and shaped the future of business analytics and data science, including the current use and applications of ML algorithms. On the other hand, machine learning does not require many assumptions and interventions when running algorithms in order to accurately predict studied outcomes [7].
There are similarities between ML and statistical modeling that are prevalent across most analytical efforts. Both techniques use historical data as input to predict new output values, but they vary as noted above on the underlying assumptions and the level of analyst intervention and data preparation.
Overall, machine learning foundations are based from statistical learning theory, and it is recommended for the data scientists to apply SLT’s guiding rules during analysis. While it may seem as a statistical background and understanding is not required when analyzing the underlying data, this misconception often leads to data scientist’s inability to set up proper research hypothesis and analysis due to a lack of understanding of the problem and the underlying data assumptions as well as caveats. This issue can in turn result in biased and irrelevant results as well as unfounded conclusions and insights. With that in mind, it is important to evaluate the problem at hand, and consider both statistical modeling and ML as possible methods to be applied. Understanding the underlying assumptions of the data and statistical inference can help support proper technique selection and guide the pathway to solution [11]. In the later sections of the chapter, application of both techniques will be provided and the reasoning for selecting the methods presented to guide future research.
As mentioned above, the similarities between ML and statistical modeling start with the underlying assumption that data or observations from the past can be used to predict the future [7]. The variables included in the analysis generally represent two types: dependent variables, that in ML are called targets, and independent variables, that in ML are called features. The definition of the variables is the same across both techniques [8]. Furthermore, both ML and statistical modeling leverage the available data in a way that allow for generalization of results to larger population [7]. The loss and risk associated with the models accuracy and representation of the real world occurrence is described frequently in terms of mean squared error (MSE). In statistical modeling, MSE is the difference between the predicted value and the actual value and is used to measure loss of the performance of predictions. In the ML, the same MSE concept is presented via a confusion matrix that evaluates a classification problem\'s accuracy [9].
4. Differences between machine learning and statistical modeling
Differences between machine learning and statistical modeling are distinct and based on purposes and needs for the analysis as well as the outcomes. Assumptions and purposes for the analysis and approach can vastly differ. For example, statistics typically assumes that predictors or features are known and additive, models are parametric, and testing of hypotheses and uncertainty are forefront. On the other hand, ML does not make these assumptions [12]. In ML, many models are based on non-parametric approaches where the structure of model is not specified or unknown, additivity is not expected, and assumptions about normal distributions, linearity or residuals, for example, are not needed for modeling [10].
The purpose of ML is predictive performance using general purpose learning algorithms to find patterns that are less known, unrelated, and in complex data without a priori view of underlying structures [10]. Whereas in statistical modeling, consideration for inferences, correlations, and the effects of a small number of variables are drivers [12].
Due to the differences in the methods’ characteristics, it is important to understand the variations in application of the techniques when solving healthcare problems. For example, one typical application of statistics is to analyze whether a population has a particular medical condition. For some diseases such as diabetes, the condition is easily screened for and diagnosed using distinct lab values, such as elevated and increasing HbA1C over time, high glucose levels and low insulin levels, often due to insulin depletion occurring from unmanaged diabetes. Also conditions such as hypertension can easily be detected at home or in the healthcare provider’s office using simple blood pressure measurement and monitoring, and wearables can identify when patients are experiencing atrial fibrillation, abnormal heart rhythms and even increased patient falls (possible syncope). Therefore, analyses of patients with these easily measurable conditions can be done simply by qualifying patients based on lab values or biomarkers falling within or outside of certain ranges. One of the simplest examples is identifying patients with diabetes [13]. This can be accomplished by using A1C levels to group patients as having no diabetes (A1C < 5.7), pre-diabetes (AIC of 5.7–6.4), or diabetes (A1C > 6.4). These ranges are based on American Diabetes Association Diagnosis Guidelines and a very high, medically accepted correlation between AIC levels and the diagnosis of diabetes [14].
On the other hand, if the objective of the research is to predict which pre-diabetic patients are most likely to progress to diabetes, a myriad of factors influence diabetes progression including extent of chronic kidney disease, high blood pressure, insulin levels over time, body mass index/obesity, age, years with diabetes, success of prior therapy, number and types of prior therapies, family history, coronary artery disease, prior cardiovascular events, infections, etc. A complicated combination of comorbidities, risk factors, and patient behavior can lead to differing diabetes complications and varying outcomes makes prediction more challenging and thus it represents a good candidate for the use of machine learning techniques. Classification models such as gradient boosting tree algorithms have been used to successfully predict diabetes progression, especially earlier in the disease. While there any many diabetes risk factors and co-morbidities, these disease characteristics are well studied over many years, thus enabling stable predictive models which perform well over time [14].
Overall, machine learning is highly effective when the model uses more than a handful of independent variables/features [10]. ML is required when the number of features (p) is larger than the number of records or observations (n) – this is called the curse of dimensionality [15, 16], which increases the risk of overfitting, but can be overcome with dimensionality reductive techniques (i.e., PCA), as part of modeling [15] and clinical/expert input on the importance or lack thereof of certain features, is it relates to the disease or its treatment. Additionally, statistical learning theory teaches that learning algorithms increase their ability to translate complex structures from data at a greater and faster rate than the increase of sample size capture can alone provide [8]. Therefore, statistical learning theory and ML offer methods for addressing high-dimensional data or big data (high velocity, volume and variety) and smaller sample sizes [17], such as recursive feature elimination and support vector machines, boosting, or cross validation which can also minimize prediction error [18].
In the healthcare industry, machine learning models are frequently used in cancer prediction, generally in three areas: (1) predicting a patient with a cancer prognosis/diagnosis, (2) predicting cancer progression, and (3) predicting cancer mortality. Of these, predicting whether a patient may have a cancer prognosis/diagnosis can be more or less difficult depending on the tumor type. Certain cancers such as lung cancer, breast cancer, prostate cancer, and skin cancer are evaluated based on specific signs and symptoms, and non-invasive imaging or blood tests. These cancers are easier to predict. Conversely, cancers with non-descript symptoms such fatigue, dizziness, GI pain and distress, and lack of appetite are much more difficult to predict even with machine learning models as these symptoms are associated with multiple tumor types (for example esophageal, stomach, bladder, liver, and pancreatic cancer) and also mimic numerous other conditions [14].
For cancers with vague symptoms, understanding the patient journey is very important to cancer prediction. If a prediction period is too long and does not reflect the time period before diagnosis when symptoms develop, the model may overfit due to spurious variables not related to the condition. If the prediction period is too short, key risk factors from the patient record could be missing. Variable pruning is required in these situations. A multi-disciplinary team including business and clinical experts can help trim unrelated variables and improve model performance [14].
Model validation is an inherent part of the ML process where the data is split into training data and test data, with the larger portion of data used to train the model to learn outputs based on known inputs. This process allows for rapid structure knowledge for primary focus on building the ability to predict future outcomes [15]. Beyond initial validation of the model within the test data set, the model should be further tested in the real world using a large, representative, and more recent sample of data [19]. This can be accomplished by using the model to score the eligible population and using a look forward period to assess incidence or prevalence of the desired outcome. If the model is performing well, probability scores should be directly correlated to incidence/prevalence (the higher the probability score, the higher the incidence/prevalence). Model accuracy, precision, and recall can also be assessed using this approach [20].
Epidemiology studies and prior published machine learning research in related areas of healthcare can help benchmark the performance of the model relative to the baseline prevalent or incident population for the condition to be predicted. Machine learning models created using a few hundred or thousand patients often do not perform as well in the real world. Careful variable pruning, cohort refinement and adjustment of modeling periods can often resolve model performance problems. Newer software can be used to more quickly build, test, and iterate models, allowing users to easily transform and combine features as well as run many models simultaneously and visualize model performance, diagnosis and solve model issues [21].
5. How to choose between machine learning and statistical modeling
Machine learning algorithms are a preferred choice of technique vs. a statistical modeling approach under specific circumstances, data configurations, and outcomes needed.
5.1 Importance of prediction over causal relationships
As noted above, machine learning algorithms are leveraged for prediction of the outcome rather than present the inferential and causal relationship between the outcome and independent variables/data elements [17, 22]. Once a model has been created, statistical analysis can sometime elucidate and validate the importance and relationship between independent and dependent variables.
5.2 Application of wide and big dataset(s)
Machine Learning algorithms are learner algorithms and learn on large amount of data often presented by a large number of data elements, but not necessarily with many observations [23]. Ability of multiple replications of samples, cross validation or application of boot strapping techniques for machine learning allows for wide datasets with many data elements and few observations, which is extremely helpful in predicting rare disease onset [24] as long as the process is accompanied with real world testing to ensure the models are not suffering from overfitting [18, 19]. With the advent of less expensive and more powerful computing power and storage, multialgorithm, ensembled models using larger cohorts can be more efficiently built. Larger modeling samples that are more representative of the overall population can help reduce the likelihood of overfitting or underfitting [25]. A large cohort imposes various issues and of priority is the ability to identify the set of independent variables that are most meaningful and impactful. These significant independent variables provide a predictive and/or inferential model that can be readily acceptable in providing a real-world application. The variables in such instances may also result into more realistic magnitude and direction of the causal relationship between the independent and outcomes variables of interest.
A recent example for a real-world example in healthcare for machine learning algorithm application is to identify the likelihood of hospitalization for high-risk patients diagnosed with Covid 19. The dataset leveraged included over 20,000 independent variables across healthcare claims data for diagnostics and treatment variables. The best optimal ML model consisted of approximately 200 important predictors variables such as age, diagnosis like Type 2 diabetes/CKD/Hypertension, frequency of office visits, Obesity amongst others. None of the variables in this example were ‘new’, however, the magnitude and direction as a result of the ML exercise may illustrate the ‘true’ impact of each independent variable, a feature that is a serious limitation in traditional statistical modeling [26].
Furthermore, as explained above, statistical models tend to not operate well on very large datasets and often require manageable datasets with a fewer number of pre-defined attributes/data elements for analysis [23]. The recommended number of attributes is up to 12 in a statistical model, because these techniques are highly prone to overfitting [25]. This limitation creates a challenge when analyzing large healthcare datasets and require application of dimension reduction techniques or expert guidance in allowing to eliminate the number of independent variables in the study [23].
5.3 Limited data and model assumptions are required
In machine learning algorithms, there are fewer assumptions that need to be made on the dataset and the data elements [5]. However, a good model is usually preceded by profiling of the target and control groups and some knowledge of the domain. Understanding relationships within the data improve outcomes and interpretability [27].
Machine learning algorithms are comparatively more flexible than statistical models, as they do not require making assumptions regarding collinearity, normal distribution of residuals, etc. [5]. Thus, they have a high tolerance for uncertainty in variable performance (e.g., confidence intervals, hypothesis tests [28]. In statistical modeling emphasis is put in uncertainty estimates, furthermore, a variety of assumptions have to be satisfied before the outcome from a statistical model can be trusted and applied [28]. As a result, the statistical models have a low uncertainty tolerance [25].
Machine learning algorithms tend to be preferred over statistical modeling when the outcome to be predicted does not have a strong component of randomness, e.g., in visual pattern recognition an object must be an E or not an E [5], and when the learning algorithm can be trained on an unlimited number of exact replications [29].
ML is also appropriate when the overall prediction is the goal, with less visibility to describe the impact of any one independent variable or the relationships between variables [30], and when estimating uncertainty in forecasts or in effects of selected predictors is not a requirement [28]. However, often data scientists and data analysts leverage regression analytics to understand the estimated impact, including directionality of the relationships between the outcome and data elements, to help with model interpretation, relevance, and validity for the studied [27]. ML is also preferred when the dataset is wide and very large [23] with underlying variables are not fully known and previously described [5].
6. Machine learning extends statistics
Machine learning requires no prior assumptions about the underlying relationships between the data elements. It is generally applied to high dimensional data sets and does not require many observations to create a working model [5]. However, understanding the underlying data will support building representative modeling cohorts, deriving features relevant for the disease state and population of interest, as well as understanding how to interpret modeling results [19, 27].
In contrast, statistical model requires a deeper understanding how the data was collected, statistical properties of the estimator (p-value, unbiased estimators), the underlying distribution of the population, etc. [17]. Statistical modeling techniques are usually applied to low dimensional data sets [25].
7. Machine learning can extend the utility of statistical modeling
Robert Tibshirani, a statistician and machine learning expert at Stanford University, calls machine learning “glorified statistics,” which presents the dependence of machine learning techniques on statistics in a successful execution that not only allows for a high level of prediction, but interpretation of the results to ensure validity and applicability of the results in the healthcare [17]. Understanding the association and knowing their differences enables data scientists and statisticians to expand their knowledge and apply variety of methods outside their domain of expertise. This is the notion of “data science,” which aims to bridge the gap between the areas as well as bring other important to consider aspects of research [5]. Data science is evolving beyond statistics or more simple ML approaches to incorporate self-learning and autonomy with the ability to interpret context, assess and fill in data gaps, and make modeling adjustment over time [31]. While these modeling approaches are not perfect and more difficult to interpret, they provide exciting new options for difficult to solve problems, especially where the underlying data or environment is rapidly changing [27].
Collaboration and communication between not only data scientists and statisticians but also medical and clinical experts, public policy creators, epidemiologists, etc. allows for designing successful research studies that not only provide predictions and insights on relationships between the vast amount of data elements and health outcomes [30], but also allow for valid, interpretable and relevant results that can be applied with confidence to the project objectives and future deployment in the real [30, 32].
Finally, it is important to remember that machine learning foundations are based in statistical theory and learning. It may seem machine learning can be done without a sound statistical background, but this leads to not really understanding the different nuances in the data and presented results [17]. Well written machine learning code does not negate the need for an in-depth understanding of the problem, assumptions, and the importance of interpretation and validation [29].
8. Specific examples in healthcare
As mentioned earlier in the chapter, machine learning algorithms can be leveraged in the healthcare industry to help evaluate a continuum of access, diagnostic and treatment outcomes, including prediction of patient diagnoses, treatment, adverse events, side effects, and improved quality of life as well as lower mortality rates [24].
As shown in Figure 1, often these algorithms can be helpful in predicting a variety of disease conditions and shortening the time from awareness to diagnosis and treatment, especially in rare and underdiagnosed conditions, estimate the ‘true’ market size, predicting disease progression such as identifying fast vs. slow progressing patients as well as determinants of suitable next line change [32]. Finally, the models can be leveraged for patient and physician segmentation and clustering to identify appropriate targets for in-person and non-personal promotion [30].
Figure 1.
Examples of Machine Learning Applications in Healthcare Analytics [22].
There are, however, instances in which machine learning might not be the right tool to leverage, including when the condition or the underlying condition have a few known variables, when the market is mature and has known predetermined diagnostic and treatment algorithm, and when understanding correlations and inference is more important than making prediction [5].
One aspect of the machine learning process is to involve a cross functional team of experts in the healthcare area to ensure that the questions and problem statement along with hypothesis are properly set up [33, 34]. Many therapeutic areas require in-depth understanding of the clinical and medical concepts (i.e., diagnostic process, treatment regimens, potential adverse effects, etc.), which can help with the research design and selection of the proper analytical techniques. If the expert knowledge is not considered or properly captured in the research design, it might lead to irrelevant, invalid, and biased results, and ultimately invalidate the entire research study [33, 34].
9. A practical guide to the predominant approach
Using a real example of a project with the goal of predicting the risk of hypertension due to underlying comorbid conditions or induced by medication, the decision to lead with machine learning vs. statistical modeling can be based on explicit criteria that can be weighed and ranked based on the desired outcome of the work [17, 32]. Please see Figure 2 presenting an example of the approach.
Figure 2.
Criteria for Choosing the Predominant Approach for a Project.
As shown in Figure 2, pending the research objectives, machine learning or statistical modeling or both techniques could be the right method(s) to apply. For example, shifts in market trends, including shifts in patient volume of diagnosis and treatment present a suitable example when a statistical modeling type of analysis should be utilized. On the other hand, trying to predict patients with a high risk for hypertension requires the utilization of ML approaches. Leveraging both methods is best suited when predictive power and explanatory reasoning is needed to understand the important factors driving the outcome and their relative magnitudes and inferences.
10. Conclusions
Machine learning requires fewer assumptions about the underlying relationships between the data elements. It is generally applied to high dimensional data sets and require fewer observations to create a working model [5]. In contrast, statistical model requires an understanding of how the data was collected, statistical properties of the estimator (p-value, unbiased estimators), the underlying distribution of the population, etc. [17]. Statistical modeling techniques are usually applied to low dimensional data sets [25]. Statistical modeling and ML are not at odds but rather complementary approaches that offer choice of techniques based on need and desired outcomes. Data scientists and analysts should not necessarily have to choose between either machine learning or statistical modeling as a mutually exclusive decision tree. Instead, selected approaches from both areas should be considered as both types of methodologies are based on the same mathematical principles but expressed somewhat differently [5, 10].
Note: This book chapter was originally posted on the Cornell University’s research working paper website: https://arxiv.org. The content of the book chapter is mostly the same compared to the version posted on https://arxiv.org [6].
Conflict of interest
The authors declare no conflict of interest.
Funding
Authors work for Symphony Health, ICON plc Organization.
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The decision on which approach to choose is often based on the problem at hand, expected outcome(s), real world application of the results and insights, as well as the availability and granularity of data for the analysis. Overall machine learning and statistical modeling are complementary techniques that are guided on similar mathematical principles, but leverage different tools to arrive at insights. Determining the best approach should consider the problem to be solved, empirical evidence and resulting hypothesis, data sources and their completeness, number of variables/data elements, assumptions, and expected outcomes such as the need for predictions or causality and reasoning. Experienced analysts and data scientists are often well versed in both types of approaches and their applications, hence use best suited tools for their analytical challenges. Due to the importance and relevance of the subject in the current analytics environment, this chapter will present an overview of each approach as well as outline their similarities and differences to provide the needed understanding when selecting the proper technique for problems at hand. Furthermore, the chapter will also provide examples of applications in the healthcare industry and outline how to decide which approach is best when analyzing healthcare data. Understanding of the best suited methodologies can help the healthcare industry to develop and apply advanced analytical tools to speed up the diagnostic and treatment processes as well as improve the quality of life for their patients.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/82063",risUrl:"/chapter/ris/82063",signatures:"Michele Bennett, Ewa J. 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Conclusions",level:"1"},{id:"sec_18",title:"Conflict of interest",level:"1"},{id:"sec_14",title:"Funding",level:"1"}],chapterReferences:[{id:"B1",body:'Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;19(13):1317-1318. DOI: 10.1001/jama.2017.18391'},{id:"B2",body:'Shelmerdine et al. Review of study reporting guidelines for clinical studies using artificial intelligence in healthcare. BMJ Health & Care Informatics. 2021;28(1):e100385. DOI: 10.1136/bmjhci-2021-100385'},{id:"B3",body:'Romano R, Gambale E. Statistics and medicine: The indispensable know-how of the researcher. Translational Medicine @UniSa. 2013;5:28-31'},{id:"B4",body:'Razzak et al. Big data analytics for preventive medicine. Neural Computing and Application. 2020;32:4417-4451. DOI: 10.1007/s00521-019-04095-y'},{id:"B5",body:'Bzdok D, Altman N, Krzywiniski M. Statistics versus machine learning. Nature Methods. 2018;15(4):233-234. DOI: 0.1038/nmeth.4642'},{id:"B6",body:'Bennett M, Hayes K, Kleczyk EJ, Mehta R. Analytics in healthcare: Similarities and differences between machine learning and traditional advanced statistical modeling. Cornell University. 2022:1-16. Available from: https://arxiv.org/abs/2201.02469'},{id:"B7",body:'Von Luxburg U, Scholkopf B. Inductive logic. In: Handbook and History of Logic. Vol. 10. New York: Elsevier; 2011'},{id:"B8",body:'Bousquet et al. Introduction to Statistical Learning. 2003. Available from: http://www.econ.upf.edu/~lugosi/mlss_slt.pdf'},{id:"B9",body:'Field A. Discovering Statistics Using R. London: Sage; 2012'},{id:"B10",body:'Carmichael I, Marron JS. Data science vs. statistics: Two cultures? Japanese Journal of Statistics and Data Science. 2018;1(1):117-138'},{id:"B11",body:'Cahn A, Shoshan A, Sagiv T, Yesharim R, Goshen R, Shalev V, et al. Prediction of progression from pre-diabetes to diabetes: Development and validation of a machine learning model. Diabetes/Metabolism Research and Reviews. 2020;36(2):e3252. DOI: 10.1002/dmrr.3252 Epub 2020 Jan 14'},{id:"B12",body:'Breiman L. Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical Science. 2001;16(3):199-231'},{id:"B13",body:'Mehta R, Uppunuthula S. Use of machine learning techniques to identify the likelihood of hospitalization for high-risk patients diagnosed with COVID-19. In: ISPOR Conference; Washington DC. 2022'},{id:"B14",body:'American Diabetes Association. Understanding A1C Diagnosis. 2022. Available from: https://www.diabetes.org/diabetes/a1c/diagnosis#:~:text=Diabetes%20is%20diagnosed%20at%20fasting,equal%20to%20126%20mg%2Fdl'},{id:"B15",body:'Bzdok et al. Machine learning: A primer. Nature Methods. 2017;14(12):1119-1120. DOI: 10.1038/nmeth.4526'},{id:"B16",body:'Bellman RE. Adaptive Control Processes. Princeton, NJ: Princeton University Press; 1961'},{id:"B17",body:'Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2ed). Stanford, CA: Springer; 2016'},{id:"B18",body:'Chapman et al. Statistical learning theory for high dimensional prediction: Application to criterion-keyed scale development. Psychology Methods. 2016;21(4):603-620. DOI: 10.1037/met0000088'},{id:"B19",body:'Argent et al. The importance of real-world validation of machine learning systems in wearable exercise biofeedback platforms: A case study. Sensors (Basel). 2021;21(7):2346. DOI: 10.3390/s21072346'},{id:"B20",body:'Parikh et al. Understanding and using sensitivity, specificity and predictive values. Indian Journal of Ophthalmology. 2008;56(1):45-50. DOI: 10.4103/0301-4738.37595'},{id:"B21",body:'Mendis A. Statistical Modeling vs. Machine Learning. 2019. Available from: https://www.kdnuggets.com/2019/08/statistical-modelling-vs-machine-learning.html'},{id:"B22",body:'Hayes K, Rajabathar R, Balasubramaniam V. Uncovering the machine learning “Black Box”: Discoveringlatent patient insights using text mining & machine learning. In: Conference Paper Presented at Innovation in Analytics via Machine Learning & AI; Las Vegas, NV. 2019 Available from: https://www.pmsa.org/other-events/past-symposia'},{id:"B23",body:'Belabbas M, Wolfe PJ. Spectral methods in machine learning and new strategies for very large datasets. Proceedings of the National Academy of Sciences. 2009;106(2):369-374. DOI: 10.1073/pnas.0810600105'},{id:"B24",body:'Kempa-Liehr et al. Healthcare pathway discovery and probabilistic machine learning. International Journal of Medical Informatics. 2020;137:104087. DOI: 10.1016/j.ijmedinf.2020.104087'},{id:"B25",body:'Wasserman L. Rise of the machines. In: Past, Present, and Future of Statistical Science. Chapman and Hall; 2013. pp. 1-12. DOI: 10.1201/b16720-49'},{id:"B26",body:'Ranjan R. Calibration in machine learning. 2019. Available from: https://medium.com/analytics-vidhya/calibration-in-machine-learning-e7972ac93555'},{id:"B27",body:'Child CM, Washburn NR. Embedding domain knowledge for machine learning of complex material systems. MRS Communications. 2019;9(3):806-820. DOI: 10.1557/mrc.2019.90'},{id:"B28",body:'Hilliermeir E, Waegerman W. Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. Machine Learning. 2021;110:457-506. DOI: 10.1007/s10994-021-05946-3'},{id:"B29",body:'Goh et al. Evaluating human versus machine learning performance in classifying research abstracts. Scientometrics. 2020;125:1197-1212. DOI: 10.1007/s11192-020-03614-2'},{id:"B30",body:'Chicco D, Jutman G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics. 2020;21(6). DOI: /10.1186/s12864-019-6413-7'},{id:"B31",body:'Ansari et al. Rethinking human-machine learning in Industry 4.0: How does the paradigm shift treat the role of human learning? Procedia Manufacturing. 2018;23:117-122. DOI: 10.1016/j.promfg.2018.04.003'},{id:"B32",body:'Morganstein et al. Predicting population health with machine learning: A scoping review. BMJ Open. 2020;10(10). DOI: 10.1136/bmjopen-2020-037860'},{id:"B33",body:'Terranova et al. Application of machine learning in translational medicine: Current status and future opportunities. The AAPS Journal. 2021;23(74). DOI: 10.1208/s12248-021-00593-x'},{id:"B34",body:'Kleczyk E, Hayes K, Bennett M. Building organization AI and ML acumen during the COVID Era. 2022. In: PMSA Annual Conference. Louisville, KY. 2022'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Michele Bennett",address:null,affiliation:'
Symphony Health, ICON, plc Organization, USA
Data Science, Computer Science, and Business Analytics, Grand Canyon University, USA
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The study of biological complexity is a new frontier that requires high-throughput molecular technology, high speed computer memory, new approaches to data analysis, and the integration of interdisciplinary skills.",book:{id:"7728",slug:"synthetic-biology-new-interdisciplinary-science",title:"Synthetic Biology",fullTitle:"Synthetic Biology - New Interdisciplinary Science"},signatures:"Karim Kadri",authors:[{id:"290766",title:"Dr.",name:"Kadri",middleName:null,surname:"Karim",slug:"kadri-karim",fullName:"Kadri Karim"}]},{id:"62059",title:"Types of HVAC Systems",slug:"types-of-hvac-systems",totalDownloads:12466,totalCrossrefCites:9,totalDimensionsCites:15,abstract:"HVAC systems are milestones of building mechanical systems that provide thermal comfort for occupants accompanied with indoor air quality. HVAC systems can be classified into central and local systems according to multiple zones, location, and distribution. 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Local systems include local heating, local air-conditioning, local ventilation, and split systems.",book:{id:"6807",slug:"hvac-system",title:"HVAC System",fullTitle:"HVAC System"},signatures:"Shaimaa Seyam",authors:[{id:"247650",title:"M.Sc.",name:"Shaimaa",middleName:null,surname:"Seyam",slug:"shaimaa-seyam",fullName:"Shaimaa Seyam"},{id:"257733",title:"MSc.",name:"Shaimaa",middleName:null,surname:"Seyam",slug:"shaimaa-seyam",fullName:"Shaimaa Seyam"},{id:"395618",title:"Dr.",name:"Shaimaa",middleName:null,surname:"Seyam",slug:"shaimaa-seyam",fullName:"Shaimaa Seyam"}]},{id:"70315",title:"Some Basic and Key Issues of Switched-Reluctance Machine Systems",slug:"some-basic-and-key-issues-of-switched-reluctance-machine-systems",totalDownloads:1268,totalCrossrefCites:0,totalDimensionsCites:1,abstract:"Although switched-reluctance machine (SRM) possesses many structural advantages and application potential, it is rather difficult to successfully control with high performance being comparable to other machines. Many critical affairs must be properly treated to obtain the improved operating characteristics. This chapter presents the basic and key technologies of switched-reluctance machine in motor and generator operations. The contents in this chapter include: (1) structures and governing equations of SRM; (2) some commonly used SRM converters; (3) estimation of key parameters and performance evaluation of SRM drive; (4) commutation scheme, current control scheme, and speed control scheme of SRM drive; (5) some commonly used front-end converters and their operation controls for SRM drive; (6) reversible and regenerative braking operation controls for SRM drive; (7) some tuning issues for SRM drive; (8) operation control and some tuning issues of switched-reluctance generators; and (9) experimental application exploration for SRM systems—(a) wind generator and microgrid and (b) EV SRM drive.",book:{id:"8899",slug:"modelling-and-control-of-switched-reluctance-machines",title:"Modelling and Control of Switched Reluctance Machines",fullTitle:"Modelling and Control of Switched Reluctance Machines"},signatures:"Chang-Ming Liaw, Min-Ze Lu, Ping-Hong Jhou and Kuan-Yu Chou",authors:[{id:"37616",title:"Prof.",name:"Chang-Ming",middleName:null,surname:"Liaw",slug:"chang-ming-liaw",fullName:"Chang-Ming Liaw"},{id:"306461",title:"Mr.",name:"Min-Ze",middleName:null,surname:"Lu",slug:"min-ze-lu",fullName:"Min-Ze Lu"},{id:"306463",title:"Mr.",name:"Ping-Hong",middleName:null,surname:"Jhou",slug:"ping-hong-jhou",fullName:"Ping-Hong Jhou"},{id:"306464",title:"Mr.",name:"Kuan-Yu",middleName:null,surname:"Chou",slug:"kuan-yu-chou",fullName:"Kuan-Yu Chou"}]}],onlineFirstChaptersFilter:{topicId:"1",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"83153",title:"Perspective Chapter: Cryptocurrencies Effectiveness for Nature",slug:"perspective-chapter-cryptocurrencies-effectiveness-for-nature",totalDownloads:0,totalDimensionsCites:null,doi:"10.5772/intechopen.106493",abstract:"The rise of cryptocurrencies based on Blockchain platforms have provided multiple solutions for social and nature projects supported by concerned investors with sustainable development initiatives. Speculation and unclear uses of a cryptocurrency plays a negative role for the projects they claim to support. A positive relationship between coin investors and supported projects must position the coin value on the scale of the community involvement among the coin and project issues, thus placing the project results above speculative moves. Coin nature and social based projects may include a decentralized autonomous organization (DAO), combined with a digital currency to contribute to social and nature improvements. This organization provides a framework for the engagement of investors, beneficiaries, and implementation partners, with results measured by reliable third parties. The potential funding from non fiduciary sources for sustainable development targets may be framed under the fundraising and financial solutions models, addressing the cryptocurrency volatility risks with responsible tokenomics in attention to transaction and regulatory issues. Overall, the more clear are the object and transaction issues of a nature conservation project supported by a currency, the more successful it will be in terms of nature and social improvements and the currency valuation for all parties involved.",book:{id:"11551",title:"Blockchain",coverURL:"https://cdn.intechopen.com/books/images_new/11551.jpg"},signatures:"Luiz Cruz Villares"},{id:"83163",title:"Robust Control Algorithm for Drones",slug:"robust-control-algorithm-for-drones",totalDownloads:0,totalDimensionsCites:0,doi:"10.5772/intechopen.105966",abstract:"Drones, also known as Crewless Aircrafts (CAs), are by far the most multi - level and multi developing technologies of the modern period. This technology has recently found various uses in the transportation area, spanning from traffic monitoring applicability to traffic engineering for overall traffic flow and efficiency improvements. Because of its non-linear characteristics and under-actuated design, the CA seems to be an excellent platform to control systems study. Following a brief overview of the system, the various evolutionary and robust control algorithms were examined, along with their benefits and drawbacks. In this chapter, a mathematical and theoretical model of a CA’s dynamics is derived, using Euler’s and Newton’s laws. The result is a linearized version of the model, from which a linear controller, the Linear Quadratic Regulator (LQR), is generated. Furthermore, the performance of these nonlinear control techniques is compared to that of the LQR. Feedback-linearization controller when implemented in the simulation for the chapter, the results for the same was better than any other algorithm when compared with. The suggested regulatory paradigm of the CA-based monitoring system and analysis study will be the subject of future research, with a particular emphasis on practical applications.",book:{id:"11522",title:"Aeronautics - New Advances",coverURL:"https://cdn.intechopen.com/books/images_new/11522.jpg"},signatures:"Parul Priya and Sushma S. Kamlu"},{id:"83171",title:"Some Results on the Non-Homogeneous Hofmann Process",slug:"some-results-on-the-non-homogeneous-hofmann-process",totalDownloads:0,totalDimensionsCites:null,doi:"10.5772/intechopen.106422",abstract:"The classical counting processes (Poisson and negative binomial) are the most traditional discrete counting processes (DCPs); however, these are based on a set of rigid assumptions. We consider a non-homogeneous counting process (which we name non-homogeneous Hofmann process – NHP) that can generate the classical counting processes (CCPs) as special cases, and also allows modeling counting processes for event history data, which usually exhibit under- or over-dispersion. We present some results of this process that will allow us to use it in other areas and establish both the probability mass function (pmf) and the cumulative distribution function (cdf) using transition intensities. This counting process (CP) will allow other researchers to work on modelling the CP, where data dispersion exists in an efficient and more flexible way.",book:{id:"12021",title:"Applied Probability Theory - New Perspectives, Recent Advances and Trends",coverURL:"https://cdn.intechopen.com/books/images_new/12021.jpg"},signatures:"Gerson Yahir Palomino Velandia and José Alfredo Jiménez Moscoso"},{id:"83152",title:"Recycled Synthetic Polymer-Based Electrospun Membranes for Filtering Applications",slug:"recycled-synthetic-polymer-based-electrospun-membranes-for-filtering-applications",totalDownloads:0,totalDimensionsCites:null,doi:"10.5772/intechopen.106683",abstract:"Synthetic polymers have been widely applied in various commercial and household applications owing to their fascinating properties of low-cost, lightweight, and processability. However, increasing population and living standards and rising demand for non-biodegradable polymers have led to the accumulation of plastic pollution resulting in the current environmental crisis. Current waste management methods such as landfilling or incineration do not solve these environmental issues. On the other hand, recycling plastic waste is the most valuable strategy for dealing with waste as raw material for high-value products. One of such products is filter membranes. Polymer fiber membranes as masks in pandemics have been one of the most sought-after products in recent years. Some types of plastic waste became a material source for the development of filter materials, which could contribute to the protection of human health. Utilizing the simple, cheap, and industrially available technological solution is also needed. Given the number of advantages, electrospinning is such a beneficial solution. The electrospun polymer waste-based membranes show excellent filtration performance and can carry many other functionalities. Therefore, this review article presents a brief overview of electrospun nanofibrous membranes based on synthetic plastic waste and summarizes the filtration performance of such membranes. This review will discuss the future perspectives of electrospun membranes as well.",book:{id:"11462",title:"Recent Developments in Nanofibers Research",coverURL:"https://cdn.intechopen.com/books/images_new/11462.jpg"},signatures:"Alena Opálková Šišková, Heba M. Abdallah, Smaher Mosad Elbayomi and Anita Eckstein Andicsová"},{id:"83166",title:"General Drag Correlations for Particle-Fluid System",slug:"general-drag-correlations-for-particle-fluid-system",totalDownloads:0,totalDimensionsCites:null,doi:"10.5772/intechopen.106427",abstract:"Particle-fluid flows are commonly encountered in industrial applications. It is of great importance to understand the fundamentals governing the behavior of such a flow system for better process design, control, and optimization. Generally, the particle-fluid flow behavior is strongly influenced by the interaction forces between fluid and particles. Among the various kinds of particle-fluid interaction forces, the drag force is the most essential. This chapter reviews the modeling of drag force for particle-fluid systems: from single particle to multiple particles, monosize to multisize, spherical to nonspherical, and Newtonian fluid to non-Newtonian fluid. Typical drag correlations in the literature are compared and assessed in terms of physical meaning, consistency, and generality.",book:{id:"11498",title:"Boundary Layer Flows - Modelling, Computation, and Applications of Laminar, Turbulent Incompressible and Compressible Flows",coverURL:"https://cdn.intechopen.com/books/images_new/11498.jpg"},signatures:"Zheng Qi, Shibo Kuang, Liangwan Rong, Kejun Dong and Aibing Yu"},{id:"82272",title:"Landslide Movement Monitoring with InSAR Technologies",slug:"landslide-movement-monitoring-with-insar-technologies",totalDownloads:2,totalDimensionsCites:0,doi:"10.5772/intechopen.105058",abstract:"Synthetic aperture radar interferometry (InSAR) is a technology that has been widely used in many areas, such as topographic mapping, land and resource survey, geological exploration, disaster prevention and mitigation, volcanic and seismic monitor and so on. Landslide, as a representative geohazard, include a wide range of phenomena involving downhill ground movement. InSAR, a technology which can measure surface deformation at the millimeter level over serveral days or years, is suitable to detect landslides with chronical and widespread movements. In this chapter, we introduce main process methods of InSAR data, including Persistent Scatter Interferometry (PSInSAR) and Distributed Scatter Interferometry (DSInSAR). A study area, Daguan County Town, one of the most landslide-prone areas in China is induced to demonstrate the practicability of InSAR in detecting landslides. Combined InSAR results with geological, geotechnical and meterological data, the distribution of landslide in Daguan County in spatial and temporal dimensions would be displayed. We also coupling numerical modeling and InSAR for characterizing landslide movements under multiple loads. The numerical results revealed that body loads dominated the cumulative downhill movements by squeezing water and air from voids, and precipitation caused seasonal movements with the direction perpendicular to the slope surface.",book:{id:"10950",title:"Landslides",coverURL:"https://cdn.intechopen.com/books/images_new/10950.jpg"},signatures:"Peifeng Ma, Yifei Cui, Weixi Wang, Hui Lin, Yuanzhi Zhang and Yi Zheng"}],onlineFirstChaptersTotal:764},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:11,numberOfPublishedChapters:91,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:108,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:333,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:11,numberOfPublishedChapters:144,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:126,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:23,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:13,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"6",title:"Infectious Diseases",doi:"10.5772/intechopen.71852",issn:"2631-6188",scope:"This series will provide a comprehensive overview of recent research trends in various Infectious Diseases (as per the most recent Baltimore classification). Topics will include general overviews of infections, immunopathology, diagnosis, treatment, epidemiology, etiology, and current clinical recommendations for managing infectious diseases. Ongoing issues, recent advances, and future diagnostic approaches and therapeutic strategies will also be discussed. This book series will focus on various aspects and properties of infectious diseases whose deep understanding is essential for safeguarding the human race from losing resources and economies due to pathogens.",coverUrl:"https://cdn.intechopen.com/series/covers/6.jpg",latestPublicationDate:"August 18th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:13,editor:{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"3",title:"Bacterial Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/3.jpg",isOpenForSubmission:!0,annualVolume:11399,editor:{id:"205604",title:"Dr.",name:"Tomas",middleName:null,surname:"Jarzembowski",slug:"tomas-jarzembowski",fullName:"Tomas Jarzembowski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKriQAG/Profile_Picture_2022-06-16T11:01:31.jpg",biography:"Tomasz Jarzembowski was born in 1968 in Gdansk, Poland. He obtained his Ph.D. degree in 2000 from the Medical University of Gdańsk (UG). After specialization in clinical microbiology in 2003, he started studying biofilm formation and antibiotic resistance at the single-cell level. In 2015, he obtained his D.Sc. degree. His later study in cooperation with experts in nephrology and immunology resulted in the designation of the new diagnostic method of UTI, patented in 2017. He is currently working at the Department of Microbiology, Medical University of Gdańsk (GUMed), Poland. Since many years, he is a member of steering committee of Gdańsk branch of Polish Society of Microbiologists, a member of ESCMID. He is also a reviewer and a member of editorial boards of a number of international journals.",institutionString:"Medical University of Gdańsk, Poland",institution:null},editorTwo:{id:"484980",title:"Dr.",name:"Katarzyna",middleName:null,surname:"Garbacz",slug:"katarzyna-garbacz",fullName:"Katarzyna Garbacz",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003St8TAQAZ/Profile_Picture_2022-07-07T09:45:16.jpg",biography:"Katarzyna Maria Garbacz, MD, is an Associate Professor at the Medical University of Gdańsk, Poland and she is head of the Department of Oral Microbiology of the Medical University of Gdańsk. She has published more than 50 scientific publications in peer-reviewed journals. She has been a project leader funded by the National Science Centre of Poland. Prof. Garbacz is a microbiologist working on applied and fundamental questions in microbial epidemiology and pathogenesis. Her research interest is in antibiotic resistance, host-pathogen interaction, and therapeutics development for staphylococcal pathogens, mainly Staphylococcus aureus, which causes hospital-acquired infections. Currently, her research is mostly focused on the study of oral pathogens, particularly Staphylococcus spp.",institutionString:"Medical University of Gdańsk, Poland",institution:null},editorThree:null},{id:"4",title:"Fungal Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/4.jpg",isOpenForSubmission:!0,annualVolume:11400,editor:{id:"174134",title:"Dr.",name:"Yuping",middleName:null,surname:"Ran",slug:"yuping-ran",fullName:"Yuping Ran",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS9d6QAC/Profile_Picture_1630330675373",biography:"Dr. Yuping Ran, Professor, Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China. Completed the Course Medical Mycology, the Centraalbureau voor Schimmelcultures (CBS), Fungal Biodiversity Centre, Netherlands (2006). International Union of Microbiological Societies (IUMS) Fellow, and International Emerging Infectious Diseases (IEID) Fellow, Centers for Diseases Control and Prevention (CDC), Atlanta, USA. Diploma of Dermatological Scientist, Japanese Society for Investigative Dermatology. Ph.D. of Juntendo University, Japan. Bachelor’s and Master’s degree, Medicine, West China University of Medical Sciences. Chair of Sichuan Medical Association Dermatology Committee. General Secretary of The 19th Annual Meeting of Chinese Society of Dermatology and the Asia Pacific Society for Medical Mycology (2013). In charge of the Annual Medical Mycology Course over 20-years authorized by National Continue Medical Education Committee of China. Member of the board of directors of the Asia-Pacific Society for Medical Mycology (APSMM). Associate editor of Mycopathologia. Vice-chief of the editorial board of Chinses Journal of Mycology, China. Board Member and Chair of Mycology Group of Chinese Society of Dermatology.",institutionString:null,institution:{name:"Sichuan University",institutionURL:null,country:{name:"China"}}},editorTwo:null,editorThree:null},{id:"5",title:"Parasitic Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/5.jpg",isOpenForSubmission:!0,annualVolume:11401,editor:{id:"67907",title:"Dr.",name:"Amidou",middleName:null,surname:"Samie",slug:"amidou-samie",fullName:"Amidou Samie",profilePictureURL:"https://mts.intechopen.com/storage/users/67907/images/system/67907.jpg",biography:"Dr. Amidou Samie is an Associate Professor of Microbiology at the University of Venda, in South Africa, where he graduated for his PhD in May 2008. He joined the Department of Microbiology the same year and has been giving lectures on topics covering parasitology, immunology, molecular biology and industrial microbiology. He is currently a rated researcher by the National Research Foundation of South Africa at category C2. He has published widely in the field of infectious diseases and has overseen several MSc’s and PhDs. His research activities mostly cover topics on infectious diseases from epidemiology to control. His particular interest lies in the study of intestinal protozoan parasites and opportunistic infections among HIV patients as well as the potential impact of childhood diarrhoea on growth and child development. He also conducts research on water-borne diseases and water quality and is involved in the evaluation of point-of-use water treatment technologies using silver and copper nanoparticles in collaboration with the University of Virginia, USA. He also studies the use of medicinal plants for the control of infectious diseases as well as antimicrobial drug resistance.",institutionString:null,institution:{name:"University of Venda",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null},{id:"6",title:"Viral Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/6.jpg",isOpenForSubmission:!0,annualVolume:11402,editor:{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. 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He previously worked as a post-doctoral fellow at the Ben-Gurion University of Negev, Israel; University of the Free State, South Africa; and Central University of Technology Bloemfontein, South Africa. He obtained his Ph.D. in Organic Chemistry from Nagaoka University of Technology, Japan. He has published more than seventy-four journal articles and attended several national and international conferences as speaker and chair. Dr. Kendrekar has received many international awards. He has several funded projects, namely, anti-malaria drug development, MRSA, and SARS-CoV-2 activity of curcumin and its formulations. He has filed four patents in collaboration with the University of Central Lancashire and Mayo Clinic Infectious Diseases. His present research includes organic synthesis, drug discovery and development, biochemistry, nanoscience, and nanotechnology.",institutionString:"Visiting Scientist at Lipid Nanostructures Laboratory, Centre for Smart Materials, School of Natural Sciences, University of Central Lancashire",institution:null},{id:"428125",title:"Dr.",name:"Vinayak",middleName:null,surname:"Adimule",slug:"vinayak-adimule",fullName:"Vinayak Adimule",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/428125/images/system/428125.jpg",biography:"Dr. Vinayak Adimule, MSc, Ph.D., is a professor and dean of R&D, Angadi Institute of Technology and Management, India. He has 15 years of research experience as a senior research scientist and associate research scientist in R&D organizations. He has published more than fifty research articles as well as several book chapters. He has two Indian patents and two international patents to his credit. Dr. Adimule has attended, chaired, and presented papers at national and international conferences. He is a guest editor for Topics in Catalysis and other journals. He is also an editorial board member, life member, and associate member for many international societies and research institutions. His research interests include nanoelectronics, material chemistry, artificial intelligence, sensors and actuators, bio-nanomaterials, and medicinal chemistry.",institutionString:"Angadi Institute of Technology and Management",institution:null},{id:"284317",title:"Prof.",name:"Kantharaju",middleName:null,surname:"Kamanna",slug:"kantharaju-kamanna",fullName:"Kantharaju Kamanna",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/284317/images/21050_n.jpg",biography:"Prof. K. Kantharaju has received Bachelor of science (PCM), master of science (Organic Chemistry) and Doctor of Philosophy in Chemistry from Bangalore University. He worked as a Executive Research & Development @ Cadila Pharmaceuticals Ltd, Ahmedabad. He received DBT-postdoc fellow @ Molecular Biophysics Unit, Indian Institute of Science, Bangalore under the supervision of Prof. P. Balaram, later he moved to NIH-postdoc researcher at Drexel University College of Medicine, Philadelphia, USA, after his return from postdoc joined NITK-Surthakal as a Adhoc faculty at department of chemistry. Since from August 2013 working as a Associate Professor, and in 2016 promoted to Profeesor in the School of Basic Sciences: Department of Chemistry and having 20 years of teaching and research experiences.",institutionString:null,institution:{name:"Rani Channamma University, Belagavi",country:{name:"India"}}},{id:"158492",title:"Prof.",name:"Yusuf",middleName:null,surname:"Tutar",slug:"yusuf-tutar",fullName:"Yusuf Tutar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/158492/images/system/158492.jpeg",biography:"Prof. Dr. Yusuf Tutar conducts his research at the Hamidiye Faculty of Pharmacy, Department of Basic Pharmaceutical Sciences, Division of Biochemistry, University of Health Sciences, Turkey. He is also a faculty member in the Molecular Oncology Program. He obtained his MSc and Ph.D. at Oregon State University and Texas Tech University, respectively. He pursued his postdoctoral studies at Rutgers University Medical School and the National Institutes of Health (NIH/NIDDK), USA. His research focuses on biochemistry, biophysics, genetics, molecular biology, and molecular medicine with specialization in the fields of drug design, protein structure-function, protein folding, prions, microRNA, pseudogenes, molecular cancer, epigenetics, metabolites, proteomics, genomics, protein expression, and characterization by spectroscopic and calorimetric methods.",institutionString:"University of Health Sciences",institution:null},{id:"180528",title:"Dr.",name:"Hiroyuki",middleName:null,surname:"Kagechika",slug:"hiroyuki-kagechika",fullName:"Hiroyuki Kagechika",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180528/images/system/180528.jpg",biography:"Hiroyuki Kagechika received his bachelor’s degree and Ph.D. in Pharmaceutical Sciences from the University of Tokyo, Japan, where he served as an associate professor until 2004. He is currently a professor at the Institute of Biomaterials and Bioengineering (IBB), Tokyo Medical and Dental University (TMDU). From 2010 to 2012, he was the dean of the Graduate School of Biomedical Science. Since 2012, he has served as the vice dean of the Graduate School of Medical and Dental Sciences. He has been the director of the IBB since 2020. Dr. Kagechika’s major research interests are the medicinal chemistry of retinoids, vitamins D/K, and nuclear receptors. He has developed various compounds including a drug for acute promyelocytic leukemia.",institutionString:"Tokyo Medical and Dental University",institution:{name:"Tokyo Medical and Dental University",country:{name:"Japan"}}},{id:"94311",title:"Prof.",name:"Martins",middleName:"Ochubiojo",surname:"Ochubiojo Emeje",slug:"martins-ochubiojo-emeje",fullName:"Martins Ochubiojo Emeje",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94311/images/system/94311.jpeg",biography:"Martins Emeje obtained a BPharm with distinction from Ahmadu Bello University, Nigeria, and an MPharm and Ph.D. from the University of Nigeria (UNN), where he received the best Ph.D. award and was enlisted as UNN’s “Face of Research.” He established the first nanomedicine center in Nigeria and was the pioneer head of the intellectual property and technology transfer as well as the technology innovation and support center. Prof. Emeje’s several international fellowships include the prestigious Raman fellowship. He has published more than 150 articles and patents. He is also the head of R&D at NIPRD and holds a visiting professor position at Nnamdi Azikiwe University, Nigeria. He has a postgraduate certificate in Project Management from Walden University, Minnesota, as well as a professional teaching certificate and a World Bank certification in Public Procurement. Prof. Emeje was a national chairman of academic pharmacists in Nigeria and the 2021 winner of the May & Baker Nigeria Plc–sponsored prize for professional service in research and innovation.",institutionString:"National Institute for Pharmaceutical Research and Development",institution:{name:"National Institute for Pharmaceutical Research and Development",country:{name:"Nigeria"}}},{id:"436430",title:"Associate Prof.",name:"Mesut",middleName:null,surname:"Işık",slug:"mesut-isik",fullName:"Mesut Işık",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/436430/images/19686_n.jpg",biography:null,institutionString:null,institution:{name:"Bilecik University",country:{name:"Turkey"}}},{id:"268659",title:"Ms.",name:"Xianquan",middleName:null,surname:"Zhan",slug:"xianquan-zhan",fullName:"Xianquan Zhan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/268659/images/8143_n.jpg",biography:"Dr. Zhan received his undergraduate and graduate training in the fields of preventive medicine and epidemiology and statistics at the West China University of Medical Sciences in China during 1989 to 1999. He received his post-doctoral training in oncology and cancer proteomics for two years at the Cancer Research Institute of Human Medical University in China. In 2001, he went to the University of Tennessee Health Science Center (UTHSC) in USA, where he was a post-doctoral researcher and focused on mass spectrometry and cancer proteomics. Then, he was appointed as an Assistant Professor of Neurology, UTHSC in 2005. He moved to the Cleveland Clinic in USA as a Project Scientist/Staff in 2006 where he focused on the studies of eye disease proteomics and biomarkers. He returned to UTHSC as an Assistant Professor of Neurology in the end of 2007, engaging in proteomics and biomarker studies of lung diseases and brain tumors, and initiating the studies of predictive, preventive, and personalized medicine (PPPM) in cancer. In 2010, he was promoted to Associate Professor of Neurology, UTHSC. Currently, he is a Professor at Xiangya Hospital of Central South University in China, Fellow of Royal Society of Medicine (FRSM), the European EPMA National Representative in China, Regular Member of American Association for the Advancement of Science (AAAS), European Cooperation of Science and Technology (e-COST) grant evaluator, Associate Editors of BMC Genomics, BMC Medical Genomics, EPMA Journal, and Frontiers in Endocrinology, Executive Editor-in-Chief of Med One. He has\npublished 116 peer-reviewed research articles, 16 book chapters, 2 books, and 2 US patents. His current main research interest focuses on the studies of cancer proteomics and biomarkers, and the use of modern omics techniques and systems biology for PPPM in cancer, and on the development and use of 2DE-LC/MS for the large-scale study of human proteoforms.",institutionString:null,institution:{name:"Xiangya Hospital Central South University",country:{name:"China"}}},{id:"40482",title:null,name:"Rizwan",middleName:null,surname:"Ahmad",slug:"rizwan-ahmad",fullName:"Rizwan Ahmad",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/40482/images/system/40482.jpeg",biography:"Dr. Rizwan Ahmad is a University Professor and Coordinator, Quality and Development, College of Medicine, Imam Abdulrahman bin Faisal University, Saudi Arabia. Previously, he was Associate Professor of Human Function, Oman Medical College, Oman, and SBS University, Dehradun. Dr. Ahmad completed his education at Aligarh Muslim University, Aligarh. He has published several articles in peer-reviewed journals, chapters, and edited books. His area of specialization is free radical biochemistry and autoimmune diseases.",institutionString:"Imam Abdulrahman Bin Faisal University",institution:{name:"Imam Abdulrahman Bin Faisal University",country:{name:"Saudi Arabia"}}},{id:"41865",title:"Prof.",name:"Farid A.",middleName:null,surname:"Badria",slug:"farid-a.-badria",fullName:"Farid A. Badria",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/41865/images/system/41865.jpg",biography:"Farid A. Badria, Ph.D., is the recipient of several awards, including The World Academy of Sciences (TWAS) Prize for Public Understanding of Science; the World Intellectual Property Organization (WIPO) Gold Medal for best invention; Outstanding Arab Scholar, Kuwait; and the Khwarizmi International Award, Iran. He has 250 publications, 12 books, 20 patents, and several marketed pharmaceutical products to his credit. He continues to lead research projects on developing new therapies for liver, skin disorders, and cancer. Dr. Badria was listed among the world’s top 2% of scientists in medicinal and biomolecular chemistry in 2019 and 2020. He is a member of the Arab Development Fund, Kuwait; International Cell Research Organization–United Nations Educational, Scientific and Cultural Organization (ICRO–UNESCO), Chile; and UNESCO Biotechnology France",institutionString:"Mansoura University",institution:{name:"Mansoura University",country:{name:"Egypt"}}},{id:"329385",title:"Dr.",name:"Rajesh K.",middleName:"Kumar",surname:"Singh",slug:"rajesh-k.-singh",fullName:"Rajesh K. Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329385/images/system/329385.png",biography:"Dr. Singh received a BPharm (2003) and MPharm (2005) from Panjab University, Chandigarh, India, and a Ph.D. (2013) from Punjab Technical University (PTU), Jalandhar, India. He has more than sixteen years of teaching experience and has supervised numerous postgraduate and Ph.D. students. He has to his credit more than seventy papers in SCI- and SCOPUS-indexed journals, fifty-five conference proceedings, four books, six Best Paper Awards, and five projects from different government agencies. He is currently an editorial board member of eight international journals and a reviewer for more than fifty scientific journals. He received Top Reviewer and Excellent Peer Reviewer Awards from Publons in 2016 and 2017, respectively. He is also on the panel of The International Reviewer for reviewing research proposals for grants from the Royal Society. He also serves as a Publons Academy mentor and Bentham brand ambassador.",institutionString:"Punjab Technical University",institution:{name:"Punjab Technical University",country:{name:"India"}}},{id:"142388",title:"Dr.",name:"Thiago",middleName:"Gomes",surname:"Gomes Heck",slug:"thiago-gomes-heck",fullName:"Thiago Gomes Heck",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/142388/images/7259_n.jpg",biography:null,institutionString:null,institution:{name:"Universidade Regional do Noroeste do Estado do Rio Grande do Sul",country:{name:"Brazil"}}},{id:"336273",title:"Assistant Prof.",name:"Janja",middleName:null,surname:"Zupan",slug:"janja-zupan",fullName:"Janja Zupan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/336273/images/14853_n.jpeg",biography:"Janja Zupan graduated in 2005 at the Department of Clinical Biochemistry (superviser prof. dr. Janja Marc) in the field of genetics of osteoporosis. Since November 2009 she is working as a Teaching Assistant at the Faculty of Pharmacy, Department of Clinical Biochemistry. In 2011 she completed part of her research and PhD work at Institute of Genetics and Molecular Medicine, University of Edinburgh. She finished her PhD entitled The influence of the proinflammatory cytokines on the RANK/RANKL/OPG in bone tissue of osteoporotic and osteoarthritic patients in 2012. From 2014-2016 she worked at the Institute of Biomedical Sciences, University of Aberdeen as a postdoctoral research fellow on UK Arthritis research project where she gained knowledge in mesenchymal stem cells and regenerative medicine. She returned back to University of Ljubljana, Faculty of Pharmacy in 2016. She is currently leading project entitled Mesenchymal stem cells-the keepers of tissue endogenous regenerative capacity facing up to aging of the musculoskeletal system funded by Slovenian Research Agency.",institutionString:null,institution:{name:"University of Ljubljana",country:{name:"Slovenia"}}},{id:"357453",title:"Dr.",name:"Radheshyam",middleName:null,surname:"Maurya",slug:"radheshyam-maurya",fullName:"Radheshyam Maurya",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/357453/images/16535_n.jpg",biography:null,institutionString:null,institution:{name:"University of Hyderabad",country:{name:"India"}}},{id:"418340",title:"Dr.",name:"Jyotirmoi",middleName:null,surname:"Aich",slug:"jyotirmoi-aich",fullName:"Jyotirmoi Aich",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000038Ugi5QAC/Profile_Picture_2022-04-15T07:48:28.png",biography:"Biotechnologist with 15 years of research including 6 years of teaching experience. Demonstrated record of scientific achievements through consistent publication record (H index = 13, with 874 citations) in high impact journals such as Nature Communications, Oncotarget, Annals of Oncology, PNAS, and AJRCCM, etc. Strong research professional with a post-doctorate from ACTREC where I gained experimental oncology experience in clinical settings and a doctorate from IGIB where I gained expertise in asthma pathophysiology. A well-trained biotechnologist with diverse experience on the bench across different research themes ranging from asthma to cancer and other infectious diseases. An individual with a strong commitment and innovative mindset. Have the ability to work on diverse projects such as regenerative and molecular medicine with an overall mindset of improving healthcare.",institutionString:"DY Patil Deemed to Be University",institution:null},{id:"349288",title:"Prof.",name:"Soumya",middleName:null,surname:"Basu",slug:"soumya-basu",fullName:"Soumya Basu",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035QxIDQA0/Profile_Picture_2022-04-15T07:47:01.jpg",biography:"Soumya Basu, Ph.D., is currently working as an Associate Professor at Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pune, Maharashtra, India. With 16+ years of trans-disciplinary research experience in Drug Design, development, and pre-clinical validation; 20+ research article publications in journals of repute, 9+ years of teaching experience, trained with cross-disciplinary education, Dr. Basu is a life-long learner and always thrives for new challenges.\r\nHer research area is the design and synthesis of small molecule partial agonists of PPAR-γ in lung cancer. She is also using artificial intelligence and deep learning methods to understand the exosomal miRNA’s role in cancer metastasis. Dr. Basu is the recipient of many awards including the Early Career Research Award from the Department of Science and Technology, Govt. of India. She is a reviewer of many journals like Molecular Biology Reports, Frontiers in Oncology, RSC Advances, PLOS ONE, Journal of Biomolecular Structure & Dynamics, Journal of Molecular Graphics and Modelling, etc. She has edited and authored/co-authored 21 journal papers, 3 book chapters, and 15 abstracts. She is a Board of Studies member at her university. She is a life member of 'The Cytometry Society”-in India and 'All India Cell Biology Society”- in India.",institutionString:"Dr. D.Y. Patil Vidyapeeth, Pune",institution:{name:"Dr. D.Y. Patil Vidyapeeth, Pune",country:{name:"India"}}},{id:"354817",title:"Dr.",name:"Anubhab",middleName:null,surname:"Mukherjee",slug:"anubhab-mukherjee",fullName:"Anubhab Mukherjee",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y0000365PbRQAU/ProfilePicture%202022-04-15%2005%3A11%3A18.480",biography:"A former member of Laboratory of Nanomedicine, Brigham and Women’s Hospital, Harvard University, Boston, USA, Dr. Anubhab Mukherjee is an ardent votary of science who strives to make an impact in the lives of those afflicted with cancer and other chronic/acute ailments. He completed his Ph.D. from CSIR-Indian Institute of Chemical Technology, Hyderabad, India, having been skilled with RNAi, liposomal drug delivery, preclinical cell and animal studies. He pursued post-doctoral research at College of Pharmacy, Health Science Center, Texas A & M University and was involved in another postdoctoral research at Department of Translational Neurosciences and Neurotherapeutics, John Wayne Cancer Institute, Santa Monica, California. In 2015, he worked in Harvard-MIT Health Sciences & Technology as a visiting scientist. He has substantial experience in nanotechnology-based formulation development and successfully served various Indian organizations to develop pharmaceuticals and nutraceutical products. He is an inventor in many US patents and an author in many peer-reviewed articles, book chapters and books published in various media of international repute. Dr. Mukherjee is currently serving as Principal Scientist, R&D at Esperer Onco Nutrition (EON) Pvt. Ltd. and heads the Hyderabad R&D center of the organization.",institutionString:"Esperer Onco Nutrition Pvt Ltd.",institution:null},{id:"319365",title:"Assistant Prof.",name:"Manash K.",middleName:null,surname:"Paul",slug:"manash-k.-paul",fullName:"Manash K. Paul",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/319365/images/system/319365.png",biography:"Manash K. Paul is a scientist and Principal Investigator at the University of California Los Angeles. He has contributed significantly to the fields of stem cell biology, regenerative medicine, and lung cancer. His research focuses on various signaling processes involved in maintaining stem cell homeostasis during the injury-repair process, deciphering the lung stem cell niche, pulmonary disease modeling, immuno-oncology, and drug discovery. He is currently investigating the role of extracellular vesicles in premalignant lung cell migration and detecting the metastatic phenotype of lung cancer via artificial intelligence-based analyses of exosomal Raman signatures. Dr. Paul also works on spatial multiplex immunofluorescence-based tissue mapping to understand the immune repertoire in lung cancer. Dr. Paul has published in more than sixty-five peer-reviewed international journals and is highly cited. He is the recipient of many awards, including the UCLA Vice Chancellor’s award and the 2022 AAISCR-R Vijayalaxmi Award for Innovative Cancer Research. He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and an editorial board member for several international journals.",institutionString:"University of California Los Angeles",institution:{name:"University of California Los Angeles",country:{name:"United States of America"}}},{id:"311457",title:"Dr.",name:"Júlia",middleName:null,surname:"Scherer Santos",slug:"julia-scherer-santos",fullName:"Júlia Scherer Santos",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/311457/images/system/311457.jpg",biography:"Dr. Júlia Scherer Santos works in the areas of cosmetology, nanotechnology, pharmaceutical technology, beauty, and aesthetics. Dr. Santos also has experience as a professor of graduate courses. Graduated in Pharmacy, specialization in Cosmetology and Cosmeceuticals applied to aesthetics, specialization in Aesthetic and Cosmetic Health, and a doctorate in Pharmaceutical Nanotechnology. Teaching experience in Pharmacy and Aesthetics and Cosmetics courses. She works mainly on the following subjects: nanotechnology, cosmetology, pharmaceutical technology, aesthetics.",institutionString:"Universidade Federal de Juiz de Fora",institution:{name:"Universidade Federal de Juiz de Fora",country:{name:"Brazil"}}},{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",slug:"abdulsamed-kukurt",fullName:"Abdulsamed Kükürt",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",biography:"Dr. Kükürt graduated from Uludağ University in Turkey. He started his academic career as a Research Assistant in the Department of Biochemistry at Kafkas University. In 2019, he completed his Ph.D. program in the Department of Biochemistry at the Institute of Health Sciences. He is currently working at the Department of Biochemistry, Kafkas University. He has 27 published research articles in academic journals, 11 book chapters, and 37 papers. He took part in 10 academic projects. He served as a reviewer for many articles. He still serves as a member of the review board in many academic journals. He is currently working on the protective activity of phenolic compounds in disorders associated with oxidative stress and inflammation.",institutionString:null,institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"178366",title:"Dr.",name:"Volkan",middleName:null,surname:"Gelen",slug:"volkan-gelen",fullName:"Volkan Gelen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178366/images/system/178366.jpg",biography:"Volkan Gelen is a Physiology specialist who received his veterinary degree from Kafkas University in 2011. Between 2011-2015, he worked as an assistant at Atatürk University, Faculty of Veterinary Medicine, Department of Physiology. 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