Types of upper-limb amputation and respective prosthesis type.
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Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
\n\nThis achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
\n\nWe are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
\n\nThank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
\n\n\n\n\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"89",leadTitle:null,fullTitle:"Search Algorithms and Applications",title:"Search Algorithms and Applications",subtitle:null,reviewType:"peer-reviewed",abstract:"Search algorithms aim to find solutions or objects with specified properties and constraints in a large solution search space or among a collection of objects. A solution can be a set of value assignments to variables that will satisfy the constraints or a sub-structure of a given discrete structure. In addition, there are search algorithms, mostly probabilistic, that are designed for the prospective quantum computer.\nThis book demonstrates the wide applicability of search algorithms for the purpose of developing useful and practical solutions to problems that arise in a variety of problem domains. Although it is targeted to a wide group of readers: researchers, graduate students, and practitioners, it does not offer an exhaustive coverage of search algorithms and applications. 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It is a removal of a limb due to medical reasons such as diseases and accidents. After this, an artificial device (prosthetics) is provided to fulfill all the desired needs. A prosthesis is an artificial device that replaces a missing body part that may be lost due to any traumatic accidents or medical reasons.
In the nineteenth century, hooks and wooden limbs were used as a replacement to fulfill the supporting needs to overcome support as well as the psychological effects experienced during the time. Prosthetic arm is a biomedical device consisting of links and joints in an open or closed system, which is also a combination of electronics. Thus, there is a need for a specified prosthetic which would help in fulfilling the requirements of the patient.
Prosthetics also come under different categories concerning the patient’s demand and desirable need. Table 1 lists the type of amputation and their respective prosthesis type.
Types of amputation | Types of prosthesis |
---|---|
Shoulder disarticulation | From shoulder below the elbow |
Transhumeral | Above elbow |
Transradial | Below elbow |
Transcarpal | Below elbow |
Types of upper-limb amputation and respective prosthesis type.
Controlling a prosthetic arm could be done in several ways. Some of them are by using Invasive Methods, which reflects a process in which an instrument is introduced in the human body. In such a process, electrodes are implanted inside the body, which would receive and implement the process. The other referred to as the non-invasive method. A non-invasion process does not introduce instruments into the body but uses the surface information to get its details and the desired output that is to be determined.
In the previous studies, various strategies have been practiced to monitor muscle activations all through activities, as reported by Lobo Prat et al. [1] To carry out a valuation of muscular contraction, sonomyography (SMG), mechanomyography (MMG) [2, 3], miokinemetric (MK), and electric impedance estimations are classically applied. Though muscular intentions and/or contraction are often determined using electromyography (EMG) and near-infrared spectroscopy (NIRS) [4], it allows continuous monitoring of the muscle during motor actions or rehabilitative movements. Further practices, such as ultrasonography [5] and lactate sampling, offer only a representation of the muscular status at the moment of the study, and not an effective trace in time.
Optical brain imaging is a frequently applied methodology in human-machine interaction technically acknowledged as functional near-infrared spectroscopy (fNIRS). It allows them to monitor the quantification of the relative changes in concentration of oxygenated and deoxygenated hemoglobin in tissue blood based on artificial diffuse spectroscopy. Functional neuroimaging suggests a non-invasive method of indirect as well as direct monitoring of brain activity. The hardware involved is portable hence making it easy to carry out experiments in any environment. fNIRS is a non-invasive brain imaging method including the quantification of chromophore concentration determined from the measurement of near-infrared (NIR) light attenuation or time-based changes [6]. It exploits the optical window in which the fundamental elements found in the human body typically cause no hindrance to infrared light of small wavelength range that is 700–900 nm. In addition to that, oxygenated hemoglobin (Hb) and deoxygenated-hemoglobin (deoxy-Hb) are strong absorbers of light which are the key components to translate the brain response [7]. The peculiarity in the absorption bands of deoxy-Hb and oxy-Hb permit the estimation of near changes in hemoglobin concentration by methods of estimating light attenuation at a couple of known wavelengths [8]. The reason behind choosing more than one wavelength is to take care of the isosbestic point that occurs at around 810 nm. At this value of light wavelength both the absorbing coefficients are indistinguishable [9].
Via the improved Beer-Lambert law [10], relative concentration is evaluated concerning the entire length covered by the light photon [11, 12]. Now, for an incident ray of light an emitter and to detect that reflected light, a detector is positioned. The distance between them is also defined. Hence, a state of brain hemodynamic is captured. The raw light concentrations that are further transformed into hemodynamic responses by the implementation of renowned Beer-Lambert Law and further utilized for feature extraction and classification. The extracted light intensity patterns can be viewed in Figure 1.
Raw fNIRS light intensity.
Several research institutions have undertaken the design and construction of robotic arms. These structures diverge depending upon the proposed utilization of the human hand. Diverse knowledge of actuation approaches has been considered and implemented. Earlier design approaches have focused on the mechanical problems of the construction and operation of the prosthetic devices. Most of these hardware devices are controlled via methods that are not natural, such as using the contraction of muscles of the opposite arm. This research attempts to lay the foundation for a scheme that can offer functionality similar to the human arm, with an intuitive technique of control.
A search carried out using Web of Science engine, to review work done in this area along with the gap identification, revealed no work done so far in this field of study.
The human arm is capable of performing seven basic motions associated with joints in the human arm. To account for transhumeral amputation, three of the main arm motions are considered i.e. one elbow and two motions affiliated with the wrist joint. These motions comprise wrist extension (WE), elbow flexion (EF), wrist supination (WS), wrist flexion (WF), elbow extension (EE), and wrist pronation (WP).
This section elaborates on the data acquisition of the defined motions. These motions were captured using The NIRsport manufactured by NIRx Technologies. It is an accessible, segmental, and robust functional near-infrared spectroscopy (fNIRS) machine that measures hemodynamic responses generated by neuro-activation of the inside brain via oxy-, deoxy-, and total hemoglobin variations in the cerebral cortex.
The updated version, NIRsport-2 proposes a host prepared to implement advancements and units to meet the requirements of wide-ranging cognitive neuroscience applications. The major advantage of this device is that it is designed to work in a rugged environment and also this device is portable. This feature aids in the development of a wearable and portable system.
A total of 15 healthy subjects were engaged who were reported to be right-hand dominant males having a mean age of 30 with a standard deviation of 4. Righthanders had been pursued to confine any dissimilarities in the hemodynamic responses because of the hemispheric domination difference [13]. All participants were chosen wisely as no one of the selected subjects had engaged in any prior study associated with brain signal acquisition experiments. None was accounted for to have a past filled with any mental, neurological, or visual affliction. Every one of them had an ordinary vision, and all signed and agreed to a composed consent after being briefed in detail regarding the test procedure. Three amputee subjects also participated in the study. Their demographics are given in Table 2.
Patient ID | A1 | A2 | A3 |
Gender | Male | Male | Male |
Age | 23 | 32 | 42 |
Amputated side | Right | Left | Right |
Residual length (cm) | 14 | 18 | 10 |
Time of amputation (months) | 7 | 24 | 145 |
Demographic characteristics of amputed subjects.
Trials employing fNIRS were permitted by the Air University Human Research Ethics Committee (HREC). These research experiments were held regarding ethical standards dictated by the world medical association in the recent declaration of Helsinki [14].
The generic methodology can be seen in Figure 2.
Generic methodology.
The specially designed fNIRS headset i.e. Easycap by NIRx technologies follows the international standard of source-detector separations i.e. 3 cm [15, 16, 17, 18, 19, 20, 21, 22, 23, 24]. After the subject wears the cap, the optodes are calibrated. The result of this calibration can be analyzed as in Figure 3. The faulty setting is shown in Figure 3(b). The boxes represent optodes. The color bar indicates if the optodes are in contact with the scalp or not and hence the colors are assigned. The white color depicts no connection between scalp and optodes. The red color indicated that the connection between the scalp and optode is critical, i.e. it needs to be adjusted. Sometimes hair comes as a hindrance and just by plugging the optode again in the cap would help establish a better connection. If the issue is not resolved by then, a clinical gel (EASYCAP Supervisc, high-viscosity electrolyte-gel) is used to make sure no hair absorbed the light. The gel is rated healthy and is safe to use with optodes. The yellow color indicates that the connection is acceptable. The signals can be acquired. In this scenario, the machine conditions are calibrated by the machine itself. The machine adds a gain factor to the optodes where the connection is acceptable and it is saved in conditions file which is used in signal processing later. The green color shows that the optodes are perfectly placed on the head surface and an excellent connection is established for data acquisition. It can be analyzed in Figure 3(a).
Visual representation of optode settings. The perfect setting is illustrated in (a) meanwhile the faulty optode settings are shown in (b). The color bar on the right side represents the signal quality class.
When the optodes are aligned, signal acquisition is started. The test strategy was segmented into training and testing. The subjects were asked to complete six tasks that were identified by fNIRS.
A comfortable chair was set up roughly 100 cm away from the subjects so that the motion cues are visible to them while the screen backlight does not interfere with the optical sensors [25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38]. This environment was set up for signal extraction. The Easycap was prepared in advance to minimize the time consumption during the optode placement process. Yet some of the detectors/sources had to be optimized during the calibration process by fixing hair via gel. The session commenced with an undeveloped span of 30 seconds to create a reference point. Later the screen indicated the participants to perform one of six definite tasks the training was additionally divided into two parts. In the first part, all the tasks were recorded sequentially, i.e. the sequence of the tasks was pre-defined. In the second stage, the subjects were demanded to perform similar motions but with random intentions. All these six tasks were logged by fNIRS. Each task comprised of 10-second trials separated by a 20-second rest session. Particulars about the experimental model are given below in Figure 4.
Experimental model for signal acquisition.
The acquired data was then processed and is briefly illustrated in the coming section.
This section explores and explains the signal processing which includes signal pre-processing, optode selection criteria, statistical feature computation, and the signal classification method to generate a control command for the control of a 3-DOF prosthetic arm designed for transhumeral amputees.
Functional near-infrared spectroscopy is the raw light intensity values recorded during a change in oxygenation and de-oxygenation of the blood in the human brain. With the help of dual-tip optodes, this concentration is recorded with two different wavelengths i.e. 760 and 850 nm. In the nirsLAB environment, the data is further processed. nirsLAB is the signal processing software that comes with the machine. nirsLAB is fully aware of the specifications and conditions applied during signal acquisition, hence the best choice for signal processing. The unwanted data is truncated along with unusual spikes or discontinuities that occurred during acquisition. It is then filtered to compute the hemodynamic states. These hemodynamic states are now utilized to extract the features.
As soon as the light intensities are acquired, they are fed to nirsLAB where first the time of stimulus is defined which in our case is 10 seconds as per the designed experimental paradigm. The data is further marked according to the conditions i.e. motion and rest.
Figure 5 represents the raw fNIRS data of both wavelengths i.e. 760 and 850 nm. It is evident that the amplitude for both data sets is different as the concentration of hemodynamic response is varying from healthy to the amputated subject. This is because of the absence of an arm. As the brain generates these signals, the unwanted responses die due to the absence of neuron carrier in the brain. The connection of arm and brain is cut because there is nothing present at the receiving end. Further, the discontinuities are removed along with the spikes if there exists any. The data is then fed for filtration. nirsLAB provides the commonly practiced filters for fNIRS data. Band-pass filter was implemented to smooth the acquired light intensities. The filtered and raw data at both the wavelengths are illustrated below in Figure 6.
The first chunk of fNIRS data according to the defined experimental paradigm is illustrated. The initial and final rest is truncated. (a) fNIRS signals of healthy subjects and (b) fNIRS signals acquired from amputee subject.
(a) The filtered data for both wavelengths after filtering can be seen in this illustration. Band-pass filter has range of 0.01–0.2 Hz. The roll-off was set to 15 as a default value. (b) The same signal is smoothed and as it is a large time-series, (c) a chunk of signal for a closer look is illustrated which is for the first 5 seconds of the activity during the task.
nirsLAB makes use of firls and filtfilt MATLAB® commands to filter the data. firls returns the parameters of a linear-phase filter, while filtfilt applies the filter parameters into the data. The latter is set to work as finite impulse response (FIR). The roll-off defines the width of the transition frequency band, i.e. how steep the transition between frequencies which are cut and frequencies which are passed for each of the upper and lower limits of frequency. The width of the transition band is calculated as Eqs. (1) and (2):
This noise-free and minimum artifact data are then used to find the hemodynamic states by using the modified Beer-Lambert Law [10, 39, 40, 41].
This light intensity raw data is then used to compute the hemodynamic response of the brain. The hemodynamic changes computed offline in nirsLAB are based on the modified Beer-Lambert law for scattering media, as mentioned above. While in nirsLAB the operator can modify all input parameters of the Beer-Lambert law (absorption coefficients and inter-optode distance), in NIRStar®, these are fixed, as they are calculated real-time. More precisely, the values for real-time ΔHbO and ΔHb computation are as follows:
Absorption coefficients are 3.843707 and 1.4865865 for 760 nm, deoxy and oxy, respectively, and for 850 nm, 1.798643 and 2.526391 deoxy and oxy, respectively.
The default inter-optode distance is set to 3.0 cm and the absorption coefficient unit is millimole per liter per centimeter (1/cm)/(mmol/L).
Mathematically, it is defined as Eq. (3)
where the variables can be defined as A: light reduction, or ∆A(λ): changes in light reduction at a given wavelength (λ); ε(λ): loss of the chromophore at a certain wavelength (λ); ∆c: changes observed in the chromophore absorption; d: distance between source and detector; DPF(λ): differential path length factor (DPF) for a certain wavelength (λ); g(λ): the scattering of the light wave at a certain wavelength (λ), where g is annulled since it is presumed to be insignificant when only light attenuation (as in continuous-wave NIRS) is considered [20, 36, 42, 43, 44, 45, 46, 47].
The differential path length factor (DPF) is a dimensionless modification factor that takes care of the increase in the optical path length that is produced by the scattering of light in organic tissue. The product of DPF and source-detector separation evaluates the “true” path length that the light has traveled inside the biological tissue cell [37, 38, 48, 49]. For NIRx technologies, this value is set constant for wavelengths 7.25/6.38 for 760/850 nm respectively.
The mathematical representation of statistical features extracted during the study is given as follows.
Signal mean (SM) was computed as Eq. (4)
where
Signal peak (SP) is defined by the change in signals amplitude among two adjacent segments which surpass a predefined threshold to reduce noise. It is given by Eq. (5)
where
The statistical features extracted from the data sample are then fed to the classifier. Classifying methods are employed to predict the motion intention. To comprehensively evaluate the performance of features, the two widely used classifiers in pattern recognition were implemented, namely, linear discriminant analysis (LDA) and artificial neural network (ANN). A generic yet comprehensive process is illustrated in Figure 7.
A generic classification process.
Fisher’s discriminant analysis or linear discriminant analysis is a method used to dimensionally contract samples of two or more classes to separate them, linearly. This classification method projects all the samples on an imaginary line which is useful for data classification. To cater for the word linear, it suggests that the classifier will dimension the given samples to represent the class information. It characterizes the resulting combinations to reduce the number of arbitrary samples by tracing a set of values in a distinct form. It anticipates the sample information so that each class is isolated without any problem. It decreases intraclass variance and increases the inter-class mean. By doing this, unlike data samples become segmented from each other and their set point shrinks together so that they cannot be mixed with other classes.
LDA is commonly used for pattern classification in offline and online systems. This technique projects all the data points on a line in such a way that each data sample that corresponds to a class is separated effectively. It decreases the intra-class variance and increases the inter-class mean. By doing this, different classes become separated from each other, and their data points get closer together so that they cannot be mixed with other classes. LDA works by maximizing the Fisher’s criterion given in Eq. (6)
Between classes scatter matrix S_B is defined as in Eq. (7)
where ni represents several samples that belong to class i, the class scatter matrix Sw is defined as in Eq. (8)
A generalized eigenvector problem can be represented as Eq. (9)
The optimal v is the eigenvector corresponding to the largest eigenvalue can be written represented as in Eq. (10) provided that Sw is nonsingular.
The classifier results were validated using the cross-validation scheme. The number of folds/layers was set to 10. It means that the entire data was mixed randomly into 10 groups, out of which nine took part to train the classifier while one remains untouched for testing purposes. This process was repeated 10 times until all groups were tested against each other.
As an initial measure, the attributes of the dataset which need to be classified or dimensionally contracted will lead to the choice of applying this method as a classifier or a dimensionality reduction algorithm to play out any desired task. The primary thought of Fisher’s analysis is fundamentally to isolate sample classes linearly moving them to an alternate feature-space. In this way, if the considered data set is linearly distinguishable, just using the algorithm as a classifier will yield better results. In any case, if the dataset is not truly distinct the classifier will attempt to sort out this dataset in another space. Yet despite every measure, the classes sample data may overlap due to the non-linear characteristic present in the sampled dataset. For this situation, there emerges a need to utilize another grouping model to manage nonlinearities governing the dataset. Hence, a neural network that comprise of hidden layers is also implemented. As for the neural network, raw data is used as input rather than featured data. This will give a broader idea of how to predict any output based on the input that have non-linear characteristic.
ANN utilizes multiple neuron layers to map data from one distribution to another for better and optimized classification. A technique called backpropagation helps ANN to learn the relationship between input and output class label. The neural network toolbox provided by MATLAB® was utilized to train the classifier. First, network topology and an activation function were defined and then weights were randomized. The model uses all training data to approximate the error of the predicted output as compared to the actual output. Then it uses the error to adjust the weights so that it could be minimized for the next training data and this process was repeated until the error was minimized. For this network we employed Relu as the activation function; the weights were initialized using the Xavier distribution, the network utilized the Adam optimizer function for gradient descent. We used 60% of data for training, and 20% for testing and validation each. A confusion matrix was generated afterward, which had a class number corresponding to each arm motion. The number of hidden layers was specified i.e. 10, and system training was initiated. Ten neurons were present in each of the intermediate hidden layer. The number of neurons in output or last layer was set to be 6, which is equal to the number of elements in the target vector.
After classifying the information, their real-time testing was performed to ensure the behavior of both classifying techniques. But bear in mind that both of these classifiers have different parameters and methodologies. They are not compared with each other here but they are implemented to grasp a comprehensive idea of how these different brain hemodynamic intentions can be evaluated. LDA and ANN were both applied separately and the outcomes are discussed in next section.
In research, the neural-machine interfaces can have a control foundation of either a single modality or via hybrid activity. This present study dwells on capturing hemodynamic responses from the human brain and generating the control command that can be translated to activate a prosthetic arm for transhumeral amputees. The results found by this particular research are discussed as follows.
These hemodynamic states are mapped using nirsLAB in Figure 8. The color bar represents the concentration of oxygenation.
The color bar represents the concentration of oxygenation. Brain activity is shown in (a) the illustration represents condition 1 i.e. motion. It can be seen that the motor region is “hot” when compared to the color bar. It depicts motion state whereas the (b) shows that the brain is undergoing significantly low or minimal hemodynamic changes hence the “cool” values depicting the condition 2 i.e. rest. The red and yellow dots seen in the motor cortex region are representations of optodes and they were positioned according to the international system.
The changes in oxygenated hemoglobin ΔHbO for all 20 channels and six activities of subject 6 are demonstrated in Figure 9. All of the optodes were not capturing the true concentration changes while brain activity was performed by the subject. Nevertheless, it was observed that similar channels were active when identical motions were executed while signal acquisition.
Optode-wise hemodynamic status visualization.
The channel outputs in Figure 8 serve to highlight the need for choosing good channels for recognizing true brain activities. According to our channel choice standard, signal averaging was used. It is understandable by human brain studies that when the right side of the human body is in motion, the left hemisphere of the human brain is activated. As in our case, the subjects were asked to move their right hand, it is obvious that the hemodynamic patterns occurring in the right hemisphere are merely noise. It can be seen in Figure 8 that all the channels of right hemisphere i.e. channels 1–10 do not show significant activity and later they were discarded while classification. The left side of the motor cortex was however active and the channels from 11 to 20 were used to extract the statistical features which took part in the classification. Features were computed spatially which allowed the overall brain activity on the left side of the motor cortex region of the human brain.
Window sizing of diverse spans has been utilized in several studies for the detection of fNIRS features [50, 51]. It is intended to minimize the window size to generate a fast response for real-time applications. So, the time spans of 0–0.5, 0–1, and 0–2 second windows were selected. These split seconds were employed for investigation of hemodynamic features to secure the best window size that will aid in decreased calculation time.
The stated performance outcomes were statistically evaluated based on the number of correctly predicted samples while the activity was completed during the period of 0–10 seconds. These actions were assessed by MATLAB® implementing the 10-fold cross-validation course. Student’s t-test was performed to establish the statistical significance of the obtained results. The confidence interval was set to 95% (p < 0.05). The quantitative comparison between healthy subjects and amputees was not possible due to a limited number of amputees. The computed p-value was 0.0337 considering a 95% confidence interval. The classification accuracies of the subjects are shown in Figure 10.
A representation of subject-wise accuracies. Healthy subjects are presented as S1–S15 whereas amputees are labeled A1–A3. All concerning classifying technique as LDA and ANN.
A confusion matrix is illustrated in Table 3 where it is evident that the wrist pronation and supination cause the most confusion. From time to time the subject executes the actions and sometimes put a break to them. In so doing the muscle intention power descend below the threshold and is not detected, and subsequently tagged as uncounted or undetected.
Output class | 1 | 2 | 3 | 4 | 5 | 6 |
---|---|---|---|---|---|---|
Target class | ||||||
1 | 0 0.0% | 0 0.0% | 0 0.0% | 0 0.0% | 0 0.0% | |
2 | 0 0.0% | 12 0.3% | 0 0.0% | 0 0.0% | 0 0.0% | |
3 | 0 0.0% | 0 0.0% | 720 14.4% | 0 0.0% | 0 0.0% | |
4 | 0 0.0% | 20 0.4% | 630 12.6% | 0 0.0% | 0 0.0% | |
5 | 0 0.0% | 0 0.0% | 0 0.0% | 0 0.0% | 0 0.0% | |
6 | 0 0.0% | 0 0.0% | 0 0.0% | 23 0.5% | 9 0.2% |
Confusion matrix of motion prediction.
In common practices, in addition to muscular fatigue, mental fatigue could likewise show up. This would affect the unwavering quality of the fNIRS signal. Frequent use of nicotine substances involving tea or coffee and weak eyesight are accounted for in such reliability. The motion prediction accuracy of an individual can be changed under the influence of such conditions.
After the results from the classifier are returned, they are translated into a control command. The motions are assigned abbreviations identical as used in Section 2. They are listed here as in Table 4.
Motion | Notation |
---|---|
Extension of elbow | EE |
Flexion of elbow | EF |
Pronation of wrist | WP |
Supination of wrist | WS |
Flexion of wrist | WF |
Class labels.
While testing, the machine responds as the variables illustrated in Table 4 that are assigned to each class motion. The controllers further single-out one definitive motion which can be analyzed while testing the data.
These control commands can be further translated to motor action via a controller such as Arduino, Raspberry Pi, Odroid, etc. as the program routine is written in a language supported by all these controllers. A three degree of freedom device can be actuated using this neural-machine interface scheme.
To eliminate the channel selection part, manufacturers are working on bundled optodes. Using these bundled optodes will not only eliminate the channel selection complications but it will also help in physical attributes as the whole head will not be covered. It will be easy to wear the head cap because of a smaller number of optodes and wires going around.
Also, using a different feature set may aid in the increase in accuracy. Rather than extracting three or four features, only one optimal feature can be evaluated. Hybridization of bio-signals can be done using advanced probability models or neural networks can be trained and implemented to hybridize different modalities.
fNIRS signals were acquired using the NIRSport machine developed by NIRx technology. These signals were recorded for six motions i.e. elbow extension (EE), elbow flexion (EF), wrist supination (WS), wrist pronation (WP), wrist extension (WE) and wrist flexion (WF) and were further analyzed. Mean and peak feature was extracted from the hemodynamic response of the brain. Also, minimum values were extracted for channel selection. The hemodynamic responses acquired from the brain were trained and tested by two widely used classifiers in pattern recognition i.e. LDA and ANN. The highest value of accuracy for an individual subject was recorded at 85% which is not yet achieved with six control commands employed by fNIRS. Both the classifiers were also active for real-time analysis. As a result of such high value of training accuracy, 8 out of 10 motions were correctly predicted in real-time setting. Possible extension of this work could be to hybridize these fNIRS signals together with another signal modality to not only increase the accuracy but also the number of control commands. Arm movement pattern for different age groups can be further explored. The number of amputed subjects could be increased to acquire data which will aid in better understanding of hemodynamic behavior of human brain and how it can be used to predict the arm motions.
We would like to mention the funding body, i.e. Higher Education Commission (HEC) of Pakistan who awarded the grant under NRPU project number 10702. We would also like to show gratitude to the friends who connected us with the amputees.
Amputation is taken from the Latin terminology “amputare” meaning to cut out. It is a removal of a limb due to medical reasons such as diseases and accidents. After this, an artificial device (prosthetics) is provided to fulfill all the desired needs. A prosthesis is an artificial device that replaces a missing body part that may be lost due to any traumatic accidents or medical reasons.
In the nineteenth century, hooks and wooden limbs were used as a replacement to fulfill the supporting needs to overcome support as well as the psychological effects experienced during the time. Prosthetic arm is a biomedical device consisting of links and joints in an open or closed system, which is also a combination of electronics. Thus, there is a need for a specified prosthetic which would help in fulfilling the requirements of the patient.
Prosthetics also come under different categories concerning the patient’s demand and desirable need. Table 1 lists the type of amputation and their respective prosthesis type.
Types of amputation | Types of prosthesis |
---|---|
Shoulder disarticulation | From shoulder below the elbow |
Transhumeral | Above elbow |
Transradial | Below elbow |
Transcarpal | Below elbow |
Types of upper-limb amputation and respective prosthesis type.
Controlling a prosthetic arm could be done in several ways. Some of them are by using Invasive Methods, which reflects a process in which an instrument is introduced in the human body. In such a process, electrodes are implanted inside the body, which would receive and implement the process. The other referred to as the non-invasive method. A non-invasion process does not introduce instruments into the body but uses the surface information to get its details and the desired output that is to be determined.
In the previous studies, various strategies have been practiced to monitor muscle activations all through activities, as reported by Lobo Prat et al. [1] To carry out a valuation of muscular contraction, sonomyography (SMG), mechanomyography (MMG) [2, 3], miokinemetric (MK), and electric impedance estimations are classically applied. Though muscular intentions and/or contraction are often determined using electromyography (EMG) and near-infrared spectroscopy (NIRS) [4], it allows continuous monitoring of the muscle during motor actions or rehabilitative movements. Further practices, such as ultrasonography [5] and lactate sampling, offer only a representation of the muscular status at the moment of the study, and not an effective trace in time.
Optical brain imaging is a frequently applied methodology in human-machine interaction technically acknowledged as functional near-infrared spectroscopy (fNIRS). It allows them to monitor the quantification of the relative changes in concentration of oxygenated and deoxygenated hemoglobin in tissue blood based on artificial diffuse spectroscopy. Functional neuroimaging suggests a non-invasive method of indirect as well as direct monitoring of brain activity. The hardware involved is portable hence making it easy to carry out experiments in any environment. fNIRS is a non-invasive brain imaging method including the quantification of chromophore concentration determined from the measurement of near-infrared (NIR) light attenuation or time-based changes [6]. It exploits the optical window in which the fundamental elements found in the human body typically cause no hindrance to infrared light of small wavelength range that is 700–900 nm. In addition to that, oxygenated hemoglobin (Hb) and deoxygenated-hemoglobin (deoxy-Hb) are strong absorbers of light which are the key components to translate the brain response [7]. The peculiarity in the absorption bands of deoxy-Hb and oxy-Hb permit the estimation of near changes in hemoglobin concentration by methods of estimating light attenuation at a couple of known wavelengths [8]. The reason behind choosing more than one wavelength is to take care of the isosbestic point that occurs at around 810 nm. At this value of light wavelength both the absorbing coefficients are indistinguishable [9].
Via the improved Beer-Lambert law [10], relative concentration is evaluated concerning the entire length covered by the light photon [11, 12]. Now, for an incident ray of light an emitter and to detect that reflected light, a detector is positioned. The distance between them is also defined. Hence, a state of brain hemodynamic is captured. The raw light concentrations that are further transformed into hemodynamic responses by the implementation of renowned Beer-Lambert Law and further utilized for feature extraction and classification. The extracted light intensity patterns can be viewed in Figure 1.
Raw fNIRS light intensity.
Several research institutions have undertaken the design and construction of robotic arms. These structures diverge depending upon the proposed utilization of the human hand. Diverse knowledge of actuation approaches has been considered and implemented. Earlier design approaches have focused on the mechanical problems of the construction and operation of the prosthetic devices. Most of these hardware devices are controlled via methods that are not natural, such as using the contraction of muscles of the opposite arm. This research attempts to lay the foundation for a scheme that can offer functionality similar to the human arm, with an intuitive technique of control.
A search carried out using Web of Science engine, to review work done in this area along with the gap identification, revealed no work done so far in this field of study.
The human arm is capable of performing seven basic motions associated with joints in the human arm. To account for transhumeral amputation, three of the main arm motions are considered i.e. one elbow and two motions affiliated with the wrist joint. These motions comprise wrist extension (WE), elbow flexion (EF), wrist supination (WS), wrist flexion (WF), elbow extension (EE), and wrist pronation (WP).
This section elaborates on the data acquisition of the defined motions. These motions were captured using The NIRsport manufactured by NIRx Technologies. It is an accessible, segmental, and robust functional near-infrared spectroscopy (fNIRS) machine that measures hemodynamic responses generated by neuro-activation of the inside brain via oxy-, deoxy-, and total hemoglobin variations in the cerebral cortex.
The updated version, NIRsport-2 proposes a host prepared to implement advancements and units to meet the requirements of wide-ranging cognitive neuroscience applications. The major advantage of this device is that it is designed to work in a rugged environment and also this device is portable. This feature aids in the development of a wearable and portable system.
A total of 15 healthy subjects were engaged who were reported to be right-hand dominant males having a mean age of 30 with a standard deviation of 4. Righthanders had been pursued to confine any dissimilarities in the hemodynamic responses because of the hemispheric domination difference [13]. All participants were chosen wisely as no one of the selected subjects had engaged in any prior study associated with brain signal acquisition experiments. None was accounted for to have a past filled with any mental, neurological, or visual affliction. Every one of them had an ordinary vision, and all signed and agreed to a composed consent after being briefed in detail regarding the test procedure. Three amputee subjects also participated in the study. Their demographics are given in Table 2.
Patient ID | A1 | A2 | A3 |
Gender | Male | Male | Male |
Age | 23 | 32 | 42 |
Amputated side | Right | Left | Right |
Residual length (cm) | 14 | 18 | 10 |
Time of amputation (months) | 7 | 24 | 145 |
Demographic characteristics of amputed subjects.
Trials employing fNIRS were permitted by the Air University Human Research Ethics Committee (HREC). These research experiments were held regarding ethical standards dictated by the world medical association in the recent declaration of Helsinki [14].
The generic methodology can be seen in Figure 2.
Generic methodology.
The specially designed fNIRS headset i.e. Easycap by NIRx technologies follows the international standard of source-detector separations i.e. 3 cm [15, 16, 17, 18, 19, 20, 21, 22, 23, 24]. After the subject wears the cap, the optodes are calibrated. The result of this calibration can be analyzed as in Figure 3. The faulty setting is shown in Figure 3(b). The boxes represent optodes. The color bar indicates if the optodes are in contact with the scalp or not and hence the colors are assigned. The white color depicts no connection between scalp and optodes. The red color indicated that the connection between the scalp and optode is critical, i.e. it needs to be adjusted. Sometimes hair comes as a hindrance and just by plugging the optode again in the cap would help establish a better connection. If the issue is not resolved by then, a clinical gel (EASYCAP Supervisc, high-viscosity electrolyte-gel) is used to make sure no hair absorbed the light. The gel is rated healthy and is safe to use with optodes. The yellow color indicates that the connection is acceptable. The signals can be acquired. In this scenario, the machine conditions are calibrated by the machine itself. The machine adds a gain factor to the optodes where the connection is acceptable and it is saved in conditions file which is used in signal processing later. The green color shows that the optodes are perfectly placed on the head surface and an excellent connection is established for data acquisition. It can be analyzed in Figure 3(a).
Visual representation of optode settings. The perfect setting is illustrated in (a) meanwhile the faulty optode settings are shown in (b). The color bar on the right side represents the signal quality class.
When the optodes are aligned, signal acquisition is started. The test strategy was segmented into training and testing. The subjects were asked to complete six tasks that were identified by fNIRS.
A comfortable chair was set up roughly 100 cm away from the subjects so that the motion cues are visible to them while the screen backlight does not interfere with the optical sensors [25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38]. This environment was set up for signal extraction. The Easycap was prepared in advance to minimize the time consumption during the optode placement process. Yet some of the detectors/sources had to be optimized during the calibration process by fixing hair via gel. The session commenced with an undeveloped span of 30 seconds to create a reference point. Later the screen indicated the participants to perform one of six definite tasks the training was additionally divided into two parts. In the first part, all the tasks were recorded sequentially, i.e. the sequence of the tasks was pre-defined. In the second stage, the subjects were demanded to perform similar motions but with random intentions. All these six tasks were logged by fNIRS. Each task comprised of 10-second trials separated by a 20-second rest session. Particulars about the experimental model are given below in Figure 4.
Experimental model for signal acquisition.
The acquired data was then processed and is briefly illustrated in the coming section.
This section explores and explains the signal processing which includes signal pre-processing, optode selection criteria, statistical feature computation, and the signal classification method to generate a control command for the control of a 3-DOF prosthetic arm designed for transhumeral amputees.
Functional near-infrared spectroscopy is the raw light intensity values recorded during a change in oxygenation and de-oxygenation of the blood in the human brain. With the help of dual-tip optodes, this concentration is recorded with two different wavelengths i.e. 760 and 850 nm. In the nirsLAB environment, the data is further processed. nirsLAB is the signal processing software that comes with the machine. nirsLAB is fully aware of the specifications and conditions applied during signal acquisition, hence the best choice for signal processing. The unwanted data is truncated along with unusual spikes or discontinuities that occurred during acquisition. It is then filtered to compute the hemodynamic states. These hemodynamic states are now utilized to extract the features.
As soon as the light intensities are acquired, they are fed to nirsLAB where first the time of stimulus is defined which in our case is 10 seconds as per the designed experimental paradigm. The data is further marked according to the conditions i.e. motion and rest.
Figure 5 represents the raw fNIRS data of both wavelengths i.e. 760 and 850 nm. It is evident that the amplitude for both data sets is different as the concentration of hemodynamic response is varying from healthy to the amputated subject. This is because of the absence of an arm. As the brain generates these signals, the unwanted responses die due to the absence of neuron carrier in the brain. The connection of arm and brain is cut because there is nothing present at the receiving end. Further, the discontinuities are removed along with the spikes if there exists any. The data is then fed for filtration. nirsLAB provides the commonly practiced filters for fNIRS data. Band-pass filter was implemented to smooth the acquired light intensities. The filtered and raw data at both the wavelengths are illustrated below in Figure 6.
The first chunk of fNIRS data according to the defined experimental paradigm is illustrated. The initial and final rest is truncated. (a) fNIRS signals of healthy subjects and (b) fNIRS signals acquired from amputee subject.
(a) The filtered data for both wavelengths after filtering can be seen in this illustration. Band-pass filter has range of 0.01–0.2 Hz. The roll-off was set to 15 as a default value. (b) The same signal is smoothed and as it is a large time-series, (c) a chunk of signal for a closer look is illustrated which is for the first 5 seconds of the activity during the task.
nirsLAB makes use of firls and filtfilt MATLAB® commands to filter the data. firls returns the parameters of a linear-phase filter, while filtfilt applies the filter parameters into the data. The latter is set to work as finite impulse response (FIR). The roll-off defines the width of the transition frequency band, i.e. how steep the transition between frequencies which are cut and frequencies which are passed for each of the upper and lower limits of frequency. The width of the transition band is calculated as Eqs. (1) and (2):
This noise-free and minimum artifact data are then used to find the hemodynamic states by using the modified Beer-Lambert Law [10, 39, 40, 41].
This light intensity raw data is then used to compute the hemodynamic response of the brain. The hemodynamic changes computed offline in nirsLAB are based on the modified Beer-Lambert law for scattering media, as mentioned above. While in nirsLAB the operator can modify all input parameters of the Beer-Lambert law (absorption coefficients and inter-optode distance), in NIRStar®, these are fixed, as they are calculated real-time. More precisely, the values for real-time ΔHbO and ΔHb computation are as follows:
Absorption coefficients are 3.843707 and 1.4865865 for 760 nm, deoxy and oxy, respectively, and for 850 nm, 1.798643 and 2.526391 deoxy and oxy, respectively.
The default inter-optode distance is set to 3.0 cm and the absorption coefficient unit is millimole per liter per centimeter (1/cm)/(mmol/L).
Mathematically, it is defined as Eq. (3)
where the variables can be defined as A: light reduction, or ∆A(λ): changes in light reduction at a given wavelength (λ); ε(λ): loss of the chromophore at a certain wavelength (λ); ∆c: changes observed in the chromophore absorption; d: distance between source and detector; DPF(λ): differential path length factor (DPF) for a certain wavelength (λ); g(λ): the scattering of the light wave at a certain wavelength (λ), where g is annulled since it is presumed to be insignificant when only light attenuation (as in continuous-wave NIRS) is considered [20, 36, 42, 43, 44, 45, 46, 47].
The differential path length factor (DPF) is a dimensionless modification factor that takes care of the increase in the optical path length that is produced by the scattering of light in organic tissue. The product of DPF and source-detector separation evaluates the “true” path length that the light has traveled inside the biological tissue cell [37, 38, 48, 49]. For NIRx technologies, this value is set constant for wavelengths 7.25/6.38 for 760/850 nm respectively.
The mathematical representation of statistical features extracted during the study is given as follows.
Signal mean (SM) was computed as Eq. (4)
where
Signal peak (SP) is defined by the change in signals amplitude among two adjacent segments which surpass a predefined threshold to reduce noise. It is given by Eq. (5)
where
The statistical features extracted from the data sample are then fed to the classifier. Classifying methods are employed to predict the motion intention. To comprehensively evaluate the performance of features, the two widely used classifiers in pattern recognition were implemented, namely, linear discriminant analysis (LDA) and artificial neural network (ANN). A generic yet comprehensive process is illustrated in Figure 7.
A generic classification process.
Fisher’s discriminant analysis or linear discriminant analysis is a method used to dimensionally contract samples of two or more classes to separate them, linearly. This classification method projects all the samples on an imaginary line which is useful for data classification. To cater for the word linear, it suggests that the classifier will dimension the given samples to represent the class information. It characterizes the resulting combinations to reduce the number of arbitrary samples by tracing a set of values in a distinct form. It anticipates the sample information so that each class is isolated without any problem. It decreases intraclass variance and increases the inter-class mean. By doing this, unlike data samples become segmented from each other and their set point shrinks together so that they cannot be mixed with other classes.
LDA is commonly used for pattern classification in offline and online systems. This technique projects all the data points on a line in such a way that each data sample that corresponds to a class is separated effectively. It decreases the intra-class variance and increases the inter-class mean. By doing this, different classes become separated from each other, and their data points get closer together so that they cannot be mixed with other classes. LDA works by maximizing the Fisher’s criterion given in Eq. (6)
Between classes scatter matrix S_B is defined as in Eq. (7)
where ni represents several samples that belong to class i, the class scatter matrix Sw is defined as in Eq. (8)
A generalized eigenvector problem can be represented as Eq. (9)
The optimal v is the eigenvector corresponding to the largest eigenvalue can be written represented as in Eq. (10) provided that Sw is nonsingular.
The classifier results were validated using the cross-validation scheme. The number of folds/layers was set to 10. It means that the entire data was mixed randomly into 10 groups, out of which nine took part to train the classifier while one remains untouched for testing purposes. This process was repeated 10 times until all groups were tested against each other.
As an initial measure, the attributes of the dataset which need to be classified or dimensionally contracted will lead to the choice of applying this method as a classifier or a dimensionality reduction algorithm to play out any desired task. The primary thought of Fisher’s analysis is fundamentally to isolate sample classes linearly moving them to an alternate feature-space. In this way, if the considered data set is linearly distinguishable, just using the algorithm as a classifier will yield better results. In any case, if the dataset is not truly distinct the classifier will attempt to sort out this dataset in another space. Yet despite every measure, the classes sample data may overlap due to the non-linear characteristic present in the sampled dataset. For this situation, there emerges a need to utilize another grouping model to manage nonlinearities governing the dataset. Hence, a neural network that comprise of hidden layers is also implemented. As for the neural network, raw data is used as input rather than featured data. This will give a broader idea of how to predict any output based on the input that have non-linear characteristic.
ANN utilizes multiple neuron layers to map data from one distribution to another for better and optimized classification. A technique called backpropagation helps ANN to learn the relationship between input and output class label. The neural network toolbox provided by MATLAB® was utilized to train the classifier. First, network topology and an activation function were defined and then weights were randomized. The model uses all training data to approximate the error of the predicted output as compared to the actual output. Then it uses the error to adjust the weights so that it could be minimized for the next training data and this process was repeated until the error was minimized. For this network we employed Relu as the activation function; the weights were initialized using the Xavier distribution, the network utilized the Adam optimizer function for gradient descent. We used 60% of data for training, and 20% for testing and validation each. A confusion matrix was generated afterward, which had a class number corresponding to each arm motion. The number of hidden layers was specified i.e. 10, and system training was initiated. Ten neurons were present in each of the intermediate hidden layer. The number of neurons in output or last layer was set to be 6, which is equal to the number of elements in the target vector.
After classifying the information, their real-time testing was performed to ensure the behavior of both classifying techniques. But bear in mind that both of these classifiers have different parameters and methodologies. They are not compared with each other here but they are implemented to grasp a comprehensive idea of how these different brain hemodynamic intentions can be evaluated. LDA and ANN were both applied separately and the outcomes are discussed in next section.
In research, the neural-machine interfaces can have a control foundation of either a single modality or via hybrid activity. This present study dwells on capturing hemodynamic responses from the human brain and generating the control command that can be translated to activate a prosthetic arm for transhumeral amputees. The results found by this particular research are discussed as follows.
These hemodynamic states are mapped using nirsLAB in Figure 8. The color bar represents the concentration of oxygenation.
The color bar represents the concentration of oxygenation. Brain activity is shown in (a) the illustration represents condition 1 i.e. motion. It can be seen that the motor region is “hot” when compared to the color bar. It depicts motion state whereas the (b) shows that the brain is undergoing significantly low or minimal hemodynamic changes hence the “cool” values depicting the condition 2 i.e. rest. The red and yellow dots seen in the motor cortex region are representations of optodes and they were positioned according to the international system.
The changes in oxygenated hemoglobin ΔHbO for all 20 channels and six activities of subject 6 are demonstrated in Figure 9. All of the optodes were not capturing the true concentration changes while brain activity was performed by the subject. Nevertheless, it was observed that similar channels were active when identical motions were executed while signal acquisition.
Optode-wise hemodynamic status visualization.
The channel outputs in Figure 8 serve to highlight the need for choosing good channels for recognizing true brain activities. According to our channel choice standard, signal averaging was used. It is understandable by human brain studies that when the right side of the human body is in motion, the left hemisphere of the human brain is activated. As in our case, the subjects were asked to move their right hand, it is obvious that the hemodynamic patterns occurring in the right hemisphere are merely noise. It can be seen in Figure 8 that all the channels of right hemisphere i.e. channels 1–10 do not show significant activity and later they were discarded while classification. The left side of the motor cortex was however active and the channels from 11 to 20 were used to extract the statistical features which took part in the classification. Features were computed spatially which allowed the overall brain activity on the left side of the motor cortex region of the human brain.
Window sizing of diverse spans has been utilized in several studies for the detection of fNIRS features [50, 51]. It is intended to minimize the window size to generate a fast response for real-time applications. So, the time spans of 0–0.5, 0–1, and 0–2 second windows were selected. These split seconds were employed for investigation of hemodynamic features to secure the best window size that will aid in decreased calculation time.
The stated performance outcomes were statistically evaluated based on the number of correctly predicted samples while the activity was completed during the period of 0–10 seconds. These actions were assessed by MATLAB® implementing the 10-fold cross-validation course. Student’s t-test was performed to establish the statistical significance of the obtained results. The confidence interval was set to 95% (p < 0.05). The quantitative comparison between healthy subjects and amputees was not possible due to a limited number of amputees. The computed p-value was 0.0337 considering a 95% confidence interval. The classification accuracies of the subjects are shown in Figure 10.
A representation of subject-wise accuracies. Healthy subjects are presented as S1–S15 whereas amputees are labeled A1–A3. All concerning classifying technique as LDA and ANN.
A confusion matrix is illustrated in Table 3 where it is evident that the wrist pronation and supination cause the most confusion. From time to time the subject executes the actions and sometimes put a break to them. In so doing the muscle intention power descend below the threshold and is not detected, and subsequently tagged as uncounted or undetected.
Output class | 1 | 2 | 3 | 4 | 5 | 6 |
---|---|---|---|---|---|---|
Target class | ||||||
1 | 0 0.0% | 0 0.0% | 0 0.0% | 0 0.0% | 0 0.0% | |
2 | 0 0.0% | 12 0.3% | 0 0.0% | 0 0.0% | 0 0.0% | |
3 | 0 0.0% | 0 0.0% | 720 14.4% | 0 0.0% | 0 0.0% | |
4 | 0 0.0% | 20 0.4% | 630 12.6% | 0 0.0% | 0 0.0% | |
5 | 0 0.0% | 0 0.0% | 0 0.0% | 0 0.0% | 0 0.0% | |
6 | 0 0.0% | 0 0.0% | 0 0.0% | 23 0.5% | 9 0.2% |
Confusion matrix of motion prediction.
In common practices, in addition to muscular fatigue, mental fatigue could likewise show up. This would affect the unwavering quality of the fNIRS signal. Frequent use of nicotine substances involving tea or coffee and weak eyesight are accounted for in such reliability. The motion prediction accuracy of an individual can be changed under the influence of such conditions.
After the results from the classifier are returned, they are translated into a control command. The motions are assigned abbreviations identical as used in Section 2. They are listed here as in Table 4.
Motion | Notation |
---|---|
Extension of elbow | EE |
Flexion of elbow | EF |
Pronation of wrist | WP |
Supination of wrist | WS |
Flexion of wrist | WF |
Class labels.
While testing, the machine responds as the variables illustrated in Table 4 that are assigned to each class motion. The controllers further single-out one definitive motion which can be analyzed while testing the data.
These control commands can be further translated to motor action via a controller such as Arduino, Raspberry Pi, Odroid, etc. as the program routine is written in a language supported by all these controllers. A three degree of freedom device can be actuated using this neural-machine interface scheme.
To eliminate the channel selection part, manufacturers are working on bundled optodes. Using these bundled optodes will not only eliminate the channel selection complications but it will also help in physical attributes as the whole head will not be covered. It will be easy to wear the head cap because of a smaller number of optodes and wires going around.
Also, using a different feature set may aid in the increase in accuracy. Rather than extracting three or four features, only one optimal feature can be evaluated. Hybridization of bio-signals can be done using advanced probability models or neural networks can be trained and implemented to hybridize different modalities.
fNIRS signals were acquired using the NIRSport machine developed by NIRx technology. These signals were recorded for six motions i.e. elbow extension (EE), elbow flexion (EF), wrist supination (WS), wrist pronation (WP), wrist extension (WE) and wrist flexion (WF) and were further analyzed. Mean and peak feature was extracted from the hemodynamic response of the brain. Also, minimum values were extracted for channel selection. The hemodynamic responses acquired from the brain were trained and tested by two widely used classifiers in pattern recognition i.e. LDA and ANN. The highest value of accuracy for an individual subject was recorded at 85% which is not yet achieved with six control commands employed by fNIRS. Both the classifiers were also active for real-time analysis. As a result of such high value of training accuracy, 8 out of 10 motions were correctly predicted in real-time setting. Possible extension of this work could be to hybridize these fNIRS signals together with another signal modality to not only increase the accuracy but also the number of control commands. Arm movement pattern for different age groups can be further explored. The number of amputed subjects could be increased to acquire data which will aid in better understanding of hemodynamic behavior of human brain and how it can be used to predict the arm motions.
We would like to mention the funding body, i.e. Higher Education Commission (HEC) of Pakistan who awarded the grant under NRPU project number 10702. We would also like to show gratitude to the friends who connected us with the amputees.
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\\n\\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
\\n\\nOpen Science is transparent and accessible knowledge that is shared and developed through collaborative networks.
\\n\\nOpen Science is about increased rigour, accountability, and reproducibility for research. It is based on the principles of inclusion, fairness, equity, and sharing, and ultimately seeks to change the way research is done, who is involved and how it is valued. It aims to make research more open to participation, review/refutation, improvement and (re)use for the world to benefit.
\\n\\nOpen Science refers to doing traditional science with more transparency involved at various stages, for example by openly sharing code and data. It implies a growing set of practices - within different disciplines - aiming at:
\\n\\nWe aim at improving the quality and availability of scholarly communication by promoting and practicing:
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The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\n\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\n\nAt IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
\n\n“By “open access” to [peer-reviewed research literature], we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited” (reference: http://www.budapestopenaccessinitiative.org)
\n\nOAI-PMH
\n\nAs a firm believer in the wider dissemination of knowledge, IntechOpen supports the Open Access Initiative Protocol for Metadata Harvesting (OAI-PMH Version 2.0). Read more
\n\nLicense
\n\nBook chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
\n\nPeer Review Policies
\n\nAll scientific works are Peer Reviewed prior to publishing. Read more
\n\nOA Publishing Fees
\n\nThe Open Access publishing model employed by IntechOpen eliminates subscription charges and pay-per-view fees, enabling readers to access research at no cost. In order to sustain operations and keep our publications freely accessible we levy an Open Access Publishing Fee for manuscripts, which helps us cover the costs of editorial work and the production of books. Read more
\n\nDigital Archiving Policy
\n\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
\n\nOpen Science is transparent and accessible knowledge that is shared and developed through collaborative networks.
\n\nOpen Science is about increased rigour, accountability, and reproducibility for research. It is based on the principles of inclusion, fairness, equity, and sharing, and ultimately seeks to change the way research is done, who is involved and how it is valued. It aims to make research more open to participation, review/refutation, improvement and (re)use for the world to benefit.
\n\nOpen Science refers to doing traditional science with more transparency involved at various stages, for example by openly sharing code and data. It implies a growing set of practices - within different disciplines - aiming at:
\n\nWe aim at improving the quality and availability of scholarly communication by promoting and practicing:
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On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. 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From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. 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After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. 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This has compromised the ability of the environment to foster life and render its intrinsic values. Heavy metals are known to be naturally occurring compounds, but anthropogenic activities introduce them in large quantities in different environmental compartments. This leads to the environment’s ability to foster life being reduced as human, animal, and plant health become threatened. This occurs due to bioaccumulation in the food chains as a result of the nondegradable state of the heavy metals. Remediation of heavy metals requires special attention to protect soil quality, air quality, water quality, human health, animal health, and all spheres as a collection. Developed physical and chemical heavy metal remediation technologies are demanding costs which are not feasible, time-consuming, and release additional waste to the environment. This chapter summarises the problems related to heavy metal pollution and various remediation technologies. A case study in South Africa mines were also used.",book:{id:"6534",slug:"heavy-metals",title:"Heavy Metals",fullTitle:"Heavy Metals"},signatures:"Vhahangwele Masindi and Khathutshelo L. Muedi",authors:[{id:"225304",title:"Dr.",name:"Vhahangwele",middleName:null,surname:"Masindi",slug:"vhahangwele-masindi",fullName:"Vhahangwele Masindi"},{id:"241403",title:"M.Sc.",name:"Khathutshelo",middleName:"Lilith",surname:"Muedi",slug:"khathutshelo-muedi",fullName:"Khathutshelo Muedi"}]},{id:"37067",doi:"10.5772/35482",title:"Fourier Transform Infrared Spectroscopy for Natural Fibres",slug:"fourier-transform-infrared-spectroscopy-for-natural-fibres",totalDownloads:9282,totalCrossrefCites:166,totalDimensionsCites:399,abstract:null,book:{id:"2270",slug:"fourier-transform-materials-analysis",title:"Fourier Transform",fullTitle:"Fourier Transform - Materials Analysis"},signatures:"Mizi Fan, Dasong Dai and Biao Huang",authors:[{id:"104647",title:"Prof.",name:"Mizi",middleName:null,surname:"Fan",slug:"mizi-fan",fullName:"Mizi Fan"}]},{id:"46243",doi:"10.5772/57255",title:"Corrosion Inhibitors – Principles, Mechanisms and Applications",slug:"corrosion-inhibitors-principles-mechanisms-and-applications",totalDownloads:13727,totalCrossrefCites:40,totalDimensionsCites:161,abstract:null,book:{id:"3817",slug:"developments-in-corrosion-protection",title:"Developments in Corrosion Protection",fullTitle:"Developments in Corrosion Protection"},signatures:"Camila G. Dariva and Alexandre F. Galio",authors:[{id:"169261",title:"Dr.",name:"Camila",middleName:"G.",surname:"Dariva",slug:"camila-dariva",fullName:"Camila Dariva"},{id:"170138",title:"Dr.",name:"Alexandre",middleName:"Ferreira",surname:"Galio",slug:"alexandre-galio",fullName:"Alexandre Galio"}]},{id:"44359",doi:"10.5772/56197",title:"Microstructure and Mechanical Properties of High Strength Two-Phase Titanium Alloys",slug:"microstructure-and-mechanical-properties-of-high-strength-two-phase-titanium-alloys",totalDownloads:10310,totalCrossrefCites:56,totalDimensionsCites:128,abstract:null,book:{id:"3494",slug:"titanium-alloys-advances-in-properties-control",title:"Titanium Alloys",fullTitle:"Titanium Alloys - Advances in Properties Control"},signatures:"J. Sieniawski, W. Ziaja, K. Kubiak and M. 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Bioremediation is an attractive and successful cleaning technique to remove toxic waste from polluted environment. Bioremediation is highly involved in degradation, eradication, immobilization, or detoxification diverse chemical wastes and physical hazardous materials from the surrounding through the all-inclusive and action of microorganisms. The main principle is degrading and converting pollutants to less toxic forms. Bioremediation can be carried out ex-situ and in-situ, depending on several factors, which include but not limited to cost, site characteristics, type, and concentration of pollutants. Hence, appropriate bioremediation technique is selected. Additionally, the major methodologies to develop bioremediation are biostimulation, bioaugmentation, bioventing, biopiles, and bioattenuation provided the environmental factors that decide the completion of bioremediation. Bioremediation is the most effective, economical, eco-friendly management tool to manage the polluted environment. All bioremediation techniques have its own advantage and disadvantage because it has its own specific applications.",book:{id:"9343",slug:"trace-metals-in-the-environment-new-approaches-and-recent-advances",title:"Trace Metals in the Environment",fullTitle:"Trace Metals in the Environment - New Approaches and Recent Advances"},signatures:"Indu Sharma",authors:[{id:"301262",title:"Associate Prof.",name:"Indu",middleName:null,surname:"Sharma",slug:"indu-sharma",fullName:"Indu Sharma"}]},{id:"60680",title:"Environmental Contamination by Heavy Metals",slug:"environmental-contamination-by-heavy-metals",totalDownloads:16234,totalCrossrefCites:187,totalDimensionsCites:405,abstract:"The environment and its compartments have been severely polluted by heavy metals. This has compromised the ability of the environment to foster life and render its intrinsic values. Heavy metals are known to be naturally occurring compounds, but anthropogenic activities introduce them in large quantities in different environmental compartments. This leads to the environment’s ability to foster life being reduced as human, animal, and plant health become threatened. This occurs due to bioaccumulation in the food chains as a result of the nondegradable state of the heavy metals. Remediation of heavy metals requires special attention to protect soil quality, air quality, water quality, human health, animal health, and all spheres as a collection. Developed physical and chemical heavy metal remediation technologies are demanding costs which are not feasible, time-consuming, and release additional waste to the environment. This chapter summarises the problems related to heavy metal pollution and various remediation technologies. A case study in South Africa mines were also used.",book:{id:"6534",slug:"heavy-metals",title:"Heavy Metals",fullTitle:"Heavy Metals"},signatures:"Vhahangwele Masindi and Khathutshelo L. Muedi",authors:[{id:"225304",title:"Dr.",name:"Vhahangwele",middleName:null,surname:"Masindi",slug:"vhahangwele-masindi",fullName:"Vhahangwele Masindi"},{id:"241403",title:"M.Sc.",name:"Khathutshelo",middleName:"Lilith",surname:"Muedi",slug:"khathutshelo-muedi",fullName:"Khathutshelo Muedi"}]},{id:"59905",title:"Synthesis of Silver Nanoparticles",slug:"synthesis-of-silver-nanoparticles",totalDownloads:6881,totalCrossrefCites:9,totalDimensionsCites:19,abstract:"Nanoparticles of noble metals, especially the silver nanoparticles, have been widely used in different fields of science. Their unique properties, which can be incorporated into biosensor materials, composite fibers, cosmetic products, antimicrobial applications, conducting materials and electronic components, make them a very important subject to be studied by chemistry, biology, healthcare, electronic and other related branches. These unique properties depend upon size and shape of the silver nanoparticles. Different preparation methods have been reported for the synthesis of the silver nanoparticles, such as electron irradiation, laser ablation, chemical reduction, biological artificial methods, photochemical methods and microwave processing. This chapter aims to inform the synthesis methods of the silver nanoparticles.",book:{id:"6552",slug:"silver-nanoparticles-fabrication-characterization-and-applications",title:"Silver Nanoparticles",fullTitle:"Silver Nanoparticles - Fabrication, Characterization and Applications"},signatures:"Remziye Güzel and Gülbahar Erdal",authors:[{id:"226613",title:"Dr.",name:"Remziye",middleName:null,surname:"Güzel",slug:"remziye-guzel",fullName:"Remziye Güzel"},{id:"240772",title:"MSc.",name:"Gülbahar",middleName:null,surname:"Erdal",slug:"gulbahar-erdal",fullName:"Gülbahar Erdal"}]},{id:"71326",title:"Stability of Metal Complexes",slug:"stability-of-metal-complexes",totalDownloads:2369,totalCrossrefCites:7,totalDimensionsCites:11,abstract:"The stability of coordination complex is an important factor that decides the stability and reactivity of a metal complex. The stability of metal complex is governed by two different aspects such as thermodynamic and kinetic stabilities. The correlation between stability and reactivity of coordination compounds has been described in this chapter. This chapter also enlists the factors influencing the stability of metal complexes such as the nature of metal ions, ligands, bonding between metal ions and ligands, etc. In addition, the methods available for the determination of stability constants are given in detail.",book:{id:"9190",slug:"stability-and-applications-of-coordination-compounds",title:"Stability and Applications of Coordination Compounds",fullTitle:"Stability and Applications of Coordination Compounds"},signatures:"Senthilkumar Muthaiah, Anita Bhatia and Muthukumar Kannan",authors:null},{id:"60518",title:"Synthetic Methods for Titanium Dioxide Nanoparticles: A Review",slug:"synthetic-methods-for-titanium-dioxide-nanoparticles-a-review",totalDownloads:5259,totalCrossrefCites:29,totalDimensionsCites:55,abstract:"Titanium dioxide (TiO2) semiconductor nanoparticles are one kind of important and promising photocatalysts in photocatalysis because of their unique optical and electronic properties. Their properties, which are determined by the preparation method, are very crucial in photocatalysis. In this chapter, an overview was carried out on the different methods that are used or have been used to prepare titanium dioxide nanoparticles. There are various methods that can be used to synthesize TiO2 and the most commonly used methods include sol-gel process, chemical vapor deposition (CVD) and hydrothermal method among others. This review will focus on selected preparation methods of titanium dioxide photocatalyst.",book:{id:"6426",slug:"titanium-dioxide-material-for-a-sustainable-environment",title:"Titanium Dioxide",fullTitle:"Titanium Dioxide - Material for a Sustainable Environment"},signatures:"Pardon Nyamukamba, Omobola Okoh, Henry Mungondori,\nRaymond Taziwa and Simcelile Zinya",authors:[{id:"196100",title:"Dr.",name:"Raymond",middleName:null,surname:"Taziwa",slug:"raymond-taziwa",fullName:"Raymond Taziwa"},{id:"219920",title:"Prof.",name:"Omobola",middleName:null,surname:"Okoh",slug:"omobola-okoh",fullName:"Omobola Okoh"},{id:"226567",title:"Dr.",name:"Pardon",middleName:null,surname:"Nyamukamba",slug:"pardon-nyamukamba",fullName:"Pardon Nyamukamba"},{id:"239758",title:"Mr.",name:"Simcelile",middleName:null,surname:"Zinya",slug:"simcelile-zinya",fullName:"Simcelile Zinya"}]}],onlineFirstChaptersFilter:{topicId:"158",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"82118",title:"Surface Hardening of Stainless Steel",slug:"surface-hardening-of-stainless-steel",totalDownloads:24,totalDimensionsCites:0,doi:"10.5772/intechopen.105036",abstract:"The addition of nitrogen to stainless steel improves mechanical and corrosion properties. Nitrogen-bearing stainless steel (HNSS) is a new corrosion-resistant alloy class exhibiting better tribological properties. High-pressure and powder metallurgy techniques were developed for the fabrication of HNSS. Solid-state routes allow nitrogen introduction through thermochemical, implantation, or plasma surface treatments. High-temperature gas nitriding (HTGN), carried out in an N2 atmosphere in the 1000°C range, allows N uptake, obtaining thick, ~0.5–1.0 wt.% N austenitic cases. HTGN is different from conventional nitriding, performed in the 500°C range, where intense CrxNy precipitation occurs, impairing the corrosion resistance. Low-temperature plasma nitriding (LTPN) introduces more N in solution, and colossal supersaturated expanded phases (~45 at.%N) are formed. N supersaturation and compressive stresses increase the hardness of the surface layer to 10–14 GPa. Ferritic, martensitic, duplex, and precipitation-hardened stainless steels can be surface-treated by LTPN, obtaining expanded ferrite and martensite. However, single LTPN stainless steel may prematurely fail when submitted to high loading, as the thin and hard expanded layers collapse due to lack of load-bearing capacity. Duplex-nitriding treatment (HTGN + LTPN) results in a thick nitrogen-rich hardened austenite substrate layer, granting mechanical support and adhesion to the expanded austenite layer.",book:{id:"11076",title:"Stainless Steels",coverURL:"https://cdn.intechopen.com/books/images_new/11076.jpg"},signatures:"André Paulo Tschiptschin and Carlos Eduardo Pinedo"},{id:"81579",title:"Welding Based Additive Manufacturing: Fundamentals",slug:"welding-based-additive-manufacturing-fundamentals",totalDownloads:30,totalDimensionsCites:0,doi:"10.5772/intechopen.104768",abstract:"Additive Manufacturing (AM) has drawn abundant attention over the past decades in the manufacturing and fabrication industries, especially to make part models and prototypes. This chapter introduces a potential welding based AM process called Wire Arc Additive Manufacturing (WAAM) for the fabrication of near-net shaped metal components including stainless steel components. To start with traditional AM processes, various fundamental traditional AM for the fabrication of components have been presented. Wire Arc Additive Manufacturing (WAAM) has been explained with its variants, synonyms, different welding processes to suit WAAM particularly to weld stainless steel metal; primary process selections for working with WAAM, important metals, and alloys that could be used in WAAM have been elaborated. A case study for WAAM fabrication of AISI 316 L stainless steel plate is included to introduce the fabrication of metal components using WAAM. Further, the most common defects which possibly play a vital role in WAAM components fabrication and a few of the future challenges regarding WAAM development are discussed. Fundamental information covered in this chapter could be more beneficial to beginners for the understanding of WAAM process generally including stainless steel component fabrication in a lucid tactic.",book:{id:"11076",title:"Stainless Steels",coverURL:"https://cdn.intechopen.com/books/images_new/11076.jpg"},signatures:"Maruthasalam Sowrirajan, Selvaraj Vijayan and Munusamy Arulraj"},{id:"80664",title:"Dependence of Corrosion Resistance of Austenitic Chromium-Nickel Steels on the Magnetic State of Austenite",slug:"dependence-of-corrosion-resistance-of-austenitic-chromium-nickel-steels-on-the-magnetic-state-of-aus",totalDownloads:59,totalDimensionsCites:0,doi:"10.5772/intechopen.102388",abstract:"Corrosive behavior of austenitic chromium-nickel steels from the magnetic state (parameter χ0) of austenite, pre-formed to interact with aggressive media are research. Correlation between the rate K of pitting corrosion and the specific magnetic susceptibility χ0 of austenite was experimentally established. It is experimentally established that the corrosion resistance of austenitic steels AISI304, 08Cr18Ni10, AISI 321, 08Cr18Ni10Тi (containing a low amount of δ-ferrite ∼0.005…0.5%) depends on the magnetic state of austenite: the corrosion rate of steel decreases with increases χ0 austenite. The tendency of change in the corrosion rate of austenitic alloy with a high nickel content 06Crh28NiMoCuTi (not contain δ-ferrite) has the opposite character: with increasing χ0, the corrosion rate of the alloy increases is revealed. For austenitic chromium-nickel steels, the corrosion rates of the individual (austenite (A), δ-ferrite (F), strain-induced α′-martensite (M)) and total (A + F, A + M and A + F + M) phases are determined. It is proposed to predict corrosion according to the specific magnetic susceptibility χ0 of austenite and the amount δ-ferrite.",book:{id:"11076",title:"Stainless Steels",coverURL:"https://cdn.intechopen.com/books/images_new/11076.jpg"},signatures:"Gennadii Snizhnoi"},{id:"80199",title:"The Evaluation of the Comparative Corrosion Behaviour of Conventional and Low-Nickel Austenitic Stainless Steel: Hercules™ Alloy",slug:"the-evaluation-of-the-comparative-corrosion-behaviour-of-conventional-and-low-nickel-austenitic-stai",totalDownloads:55,totalDimensionsCites:0,doi:"10.5772/intechopen.102381",abstract:"Austenitic stainless steels require approximately 8% Ni to maintain austenitic microstructure at room temperature for alloys such as 304 stainless steel (304SS). Ni contributes approximately 60% of the total material cost and its price fluctuates, making the cost of austenitic stainless steel unpredictable. The use of low-nickel austenitic stainless steels as a substitute has been considered in order to remedy costs associated with Ni price fluctuations. Alloying elements such as Mn and N have been considered, however they have been found to reduce corrosion resistance. A new alloy namely Hercules™ has been developed with reduced Ni content (1.8–2% Ni). This chapter presents a comparative study of the corrosion behavior of Hercules™ and 304SS in different solutions. The alloys were evaluated using cyclic polarisation technique and immersion tests. The results demonstrated that the corrosion resistance of Hercules™ is comparable to that of 304SS. This presents the alloys as potential industrial substitutes of each other.",book:{id:"11076",title:"Stainless Steels",coverURL:"https://cdn.intechopen.com/books/images_new/11076.jpg"},signatures:"Duduzile Nkomo and Nomsombuluko Masia"},{id:"80346",title:"Nitrogen Supersaturation of AISI316 Base Stainless Steels at 673 K and 623 K for Hardening and Microstructure Control",slug:"nitrogen-supersaturation-of-aisi316-base-stainless-steels-at-673-k-and-623-k-for-hardening-and-micro",totalDownloads:59,totalDimensionsCites:1,doi:"10.5772/intechopen.102387",abstract:"The high-density plasma nitriding at 673 K and 623 K was employed to make 10% of nitrogen supersaturation on AISI316 base austenitic stainless steels. The processing parameters and nitrogen-hydrogen gas flow ratio were optimized to increase the yield of N2+ ion and NH-radical for efficient nitriding. The nitrided AISI316 specimens were prepared for multidimensional analysis to describe the fundamental features of low-temperature plasma nitriding. First, macroscopic evaluation revealed that nitrogen supersaturation induced the γ-lattice expansion and the higher nitrogen content than 4% of mass in depth. The mesoscopic analysis describes the holding temperature and initial grain-size effects on the microstructure changes. Plastic straining, grain-size refinement, and nitrogen zone-boundary diffusion processes advance with nitrogen supersaturation to drive the inner nitriding behavior. The microscopic analysis explains the microstructure refinement, the two-phase structuring, and the microstructure modification. Through this multi-dimensional analysis, the essential characteristics of the low-temperature plasma nitriding of 316 austenitic stainless steels were precisely understood to extend the engineering treatise on the bulk nitrogen stainless steels for surface modification and treatment of stainless steels by nitriding. This plasma nitriding was applied to strengthen and harden the AISI316 wire surfaces toward its application on surgery wires.",book:{id:"11076",title:"Stainless Steels",coverURL:"https://cdn.intechopen.com/books/images_new/11076.jpg"},signatures:"Tatsuhiko Aizawa, Tomomi Shiratori, Tomoaki Yoshino, Yohei Suzuki and Takafumi Komatsu"},{id:"79904",title:"Corrosion Resistance, Evaluation Methods, and Surface Treatments of Stainless Steels",slug:"corrosion-resistance-evaluation-methods-and-surface-treatments-of-stainless-steels",totalDownloads:106,totalDimensionsCites:1,doi:"10.5772/intechopen.101430",abstract:"Stainless steels are widely recognized and find applications in many engineering industries and companies due to their excellent properties including high resistance to corrosion as a result of their minimum 10.5% chromium content, exceptional strength and durability, temperature resistance, high recyclability, and easy formability. In the present book chapter, the basic concepts of stainless steel including its applications, classifications, and corrosion properties will first be discussed. Thereafter, their corrosion behaviour will then be explained. The various methods by which the corrosion resistance behaviour can be significantly improved including surface treatments such as coatings/electrodepositions, alloying, mechanical treatment, and others will be discussed in detail.",book:{id:"11076",title:"Stainless Steels",coverURL:"https://cdn.intechopen.com/books/images_new/11076.jpg"},signatures:"Temitope Olumide Olugbade"}],onlineFirstChaptersTotal:8},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:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:18,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:9,numberOfPublishedChapters:139,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:122,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,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:21,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:10,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. 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(Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. 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He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",institutionURL:null,country:{name:"Spain"}}}]},{type:"book",id:"7656",title:"Fuzzy Logic",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7656.jpg",slug:"fuzzy-logic",publishedDate:"February 5th 2020",editedByType:"Edited by",bookSignature:"Constantin Volosencu",hash:"54f092d4ffe0abf5e4172a80025019bc",volumeInSeries:3,fullTitle:"Fuzzy Logic",editors:[{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. 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