\r\n\tWith a history of over 50 years since their introduction into therapy and formulation of medicinal products, hydrogels remain a challenge for researchers in the field. \r\n\tVersatile, with high-water content, tunable properties, and mild processing conditions, hydrogels advanced from simple chemically or physically crosslinked networks to complex double network composites or even more sophisticated new developments as shape memory and self-healing hydrogels. \r\n\tIncreasing knowledge in hybrid or composite hydrogel materials, controlled release of sensitive drugs, or several drugs from the same hydrogel matrix could be achieved. Parallel to targeted efforts aimed to maintain drug micro- or nanoparticle’s distinct three-dimensional structure, synergistic hybrid materials with more than one type of polymer was developed.
\r\n
\r\n\tBut one of the most challenging tasks remains further and continues to improve the clinical translation of these innovative hydrogels. That is what this book intends to provide the reader: a comprehensive overview of the current state-of-the-art, recent advances, new perspectives, and applications of the hydrogels as valuable platforms for targeted delivery. Driven by the need to ensure proper patient compliance, ease of administration, along with the possibility to modulate release and degradation profiles after administration, numerous non-topical hydrogel formulations had been reported. Smart and supramolecular hydrogels, stimuli-reactive materials, that quickly respond in mild conditions, represent today an attractive approach for minimally invasive treatments.
\r\n
\r\n\tThe book will also represent an invitation to discover “new” off-the-shelf hydrogels with highly tunable properties, with low complexity of formulation (environmentally friendly processing), but with adequate features to fulfill clinical requirements and provide desired delivery platforms for therapy.
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Professor POPA LĂCRĂMIOARA (h-index 14) received her Ph.D. in Pharmacy (2000). She has over 28 years of experience in physical pharmacy: Quality by Design (QbD) in the development, analysis, and optimization of pharmaceutical systems; characterization of the raw materials surfaces and pharmaceutical systems; polymeric materials with biomedical applications.",coeditorOneBiosketch:"A pioneering researcher in Taguchi's approach for the development and optimization of biomaterial-based drug delivery systems. Professor Mihaela Violeta Ghica (h-index 16) has over 19 years ‘experience in physical pharmacy: modern methods of experimental statistical design in the development of drug delivery systems and technological processes optimization; biomaterials for tissue regeneration: obtaining, physical-chemical, biopharmaceutical, structural and morphological characterization.",coeditorTwoBiosketch:"A pioneering researcher in nanostructures formulation in pharmaceutical sciences. Professor Cristina-Elena Dinu-Pîrvu (h-index 15) is a member of the Romanian National Council for the Attestation of University Titles, Diplomas, and Certificates–Commission Pharmacy. She has over 28 years of experience in physical pharmacy: development of smart, efficient, and safe biocompatible materials; development of bio- and nanostructures for diagnostic and therapy.",coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"228211",title:"Prof.",name:"Lacramioara",middleName:null,surname:"Popa",slug:"lacramioara-popa",fullName:"Lacramioara Popa",profilePictureURL:"https://mts.intechopen.com/storage/users/228211/images/system/228211.jpeg",biography:"Professor POPA LĂCRĂMIOARA received her PhD in Pharmacy (2000). She is head of Physical and Colloidal Chemistry Department, Faculty of Pharmacy, 'Carol Davila” University of Medicine and Pharmacy from Bucharest, Romania and President of the Ethics and Quality Assurance Commission from the same faculty. She is PhD supervisor (Habilitation thesis, 2014). Professor Popa Lăcrămioara graduated several courses in Intellectual Property (WIPO Academy, Geneva). She has over 28 years’ experience in physical pharmacy: Quality by Design (QbD) in the development, analysis, and optimization of pharmaceutical systems; characterization of the raw materials surfaces and pharmaceutical systems; polymeric materials with biomedical applications. 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She holds a Master’s degree in Physical Chemistry and Applied Radiochemistry at the Faculty of Chemistry from University of Bucharest.\nShe has over 17 years of experience in: design, development, physico-chemical and biopharmaceutical evaluation of drug delivery systems with conventional/modified/ controlled/targeted release for topical application; use of statistic experimental design techniques combined with response surface methodology and Taguchi approach for the optimization of the pharmaceutical systems formulation and some technological processes; valorisation of the biopharmacological potential of the medicinal and aromatic plants. Professor Mihaela Violeta Ghica was project director and partner responsible for some national and international projects. 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\n
1. Introduction
\n
Nowadays industrial applications are straightly involved with intelligent manufacturing processes, and the importance of this issue is reflected in many different activities of the human being, for example, in health, economy, and even comfort. Thus, it is possible to say that most of the daily activities carried out by humans have a direct relationship with those elements produced in the industry that facilitate its making. On the other hand, during the last years, industrial sites have been continuously subjected to several transformations, aiming to improve the effectiveness of its processes and to increase the production quality. Consequently, the integration of multiple technologies in the industry has been performed by the composition of actuators and sensors with cyber-physical systems and Internet of Things devices. Such integration leads to the Industry 4.0 that is the fourth phase of manufacturing and industrial sectors where the automated manufacturing and process monitoring have been enhanced [1]. Consequently, under the integration of such complex systems, it should be highlighted that it is important to ensure its safety and reliability by the implementation of condition-based monitoring approaches. Thereby, in order to guarantee the proper operation in manufacturing processes and aiming to avoid undesirable downtimes, the working condition of the machine components must be continuously assessed. Commonly, most of the industrial applications and processes are involved with the use of mechanical and electrical rotating machines, where electric motors and gearboxes represent the most used elements to perform specific manufacturing processes [2].
\n
In fact, this statement is validated and justified because electric motors, gearboxes, couplings, and shafts represent approximately more than 90% of the elements that compose any industrial process [3]. Indeed, these elements that integrate the main operating system of industrial machinery are also considered, and also known, as the electromechanical machine system. In this sense, electric motors are considered as the most important element in electromechanical systems since its performed functions cannot be carried out and replaced by any other element; additionally, these elements play also an important role in most of the industrial applications because two-thirds of the total electricity is consumed by them. Therefore, these issues make suitable the application of condition monitoring approaches to avoid the occurrence of unexpected breakdowns; even more, it must be noted that under the appearance of a faulty condition, such damaged element can also have influence over the proper operation of the whole elements that are linked to the electromechanical system and crucial damages may be produced [4].
\n
As it has been mentioned, industrial sites have been subjected to several transformations, and through the integration of multiple technologies, a significant improvement in the production efficiency has been obtained. Accordingly, complex electromechanical systems compose most of the industrial machinery that is used in different applications of modern industry. In this regard, several condition monitoring-based approaches have been developed aiming to guarantee the appropriate working condition of industrial machinery. Thus, data-driven condition monitoring strategies represent the most common and suitable approach for carrying out the condition assessment in electromechanical systems; this approach has been preferred since it only takes into account the use of information of available data; therefore, based on known and available information, an accurate diagnosis of the machine under inspection is obtained [2, 4, 5].
\n
In this sense, most of the data-driven approaches mainly include the continuous monitoring of different physical magnitudes that contain significant information related to the machine working condition. Indeed, stator current signals, vibrations, temperatures, and operational rotating speeds, among others, are some of the most accepted and reliable magnitudes used in condition monitoring strategies. On the other hand, aiming to provide the condition assessment, such monitored signals are then analyzed by different signal processing techniques, where time-domain analysis, frequency-domain analysis, and time-frequency domain analysis have been commonly implemented in several condition monitoring strategies [5]. However, although there exist different signal processing, it has been demonstrated that statistical time-based domain features contain significant information that describes the behavior related to the rotating machine working condition. Thereby, the calculation of a high-performance set of features is achieved because statistical time domain-based features have advantages for describing changes and trends of time-domain signals [6].
\n
On the other side, although other sophisticated techniques such as fast Fourier transform (FFT) and discrete wavelet transforms (DWT), among others, also lead to the calculation of features related to the machine condition, the implementation of such techniques considers additional knowledge and experience about the proper usage of the techniques and also complete information of the parameters of the machine operation. Accordingly, it should be highlighted that it is not totally true that sophisticated and complex signal processing may always lead to the estimation of the most representative set of features to describe the machine condition. In this regard, from a practical application viewpoint and based on practical experience, the simplest way to evaluate and identify the early occurrence of faults is by means of analyzing trends of physical magnitudes acquired during the continuous working operation of the machine. Thus, as aforementioned, the appropriate early detection of faults may help in the reduction of monetary losses caused by unscheduled maintenance task.
\n
Certainly, the detection and identification of faulty operating modes involve a critical procedure in which the signal processing or feature calculation must be carefully performed. Another important issue to perform and improve the condition assessment is the consideration of artificial intelligence (AI) for carrying out the automatic fault diagnosis. Indeed, the use of AI in condition monitoring strategies has been rapidly increased, and its application to identify the occurrence of faults in rotating machinery is an adequate and coherent option to obtain high-performance results. Additionally, it has been shown that an appropriated application of AI in condition monitoring approaches provides a powerful capacity for detecting and classifying the appearance of single or multiple faults in electromechanical systems. This potential provided by AI is reached because the limitations of classical space-transform techniques, when nonlinearities characterize the analyzed system, are overcome [6].
\n
Hence, several AI techniques have been addressed with the main purpose of being applied in monitoring tasks of industrial machinery, for instance, artificial neural networks, genetic algorithms, fuzzy logic, support vector machines, Bayesian networks, self-organizing maps (SOM), and case-based reasoning, among others, represent some of the most techniques used in condition monitoring approaches [7]. However, there are still great challenges for developing new condition monitoring strategies; indeed, the use of AI techniques has increased because the main challenge of the condition assessment in industrial sites is that nonlinearities are inherent to the working operation [8].
\n
Thereby, the contribution of this chapter lies in the proposal of a condition monitoring strategy for detecting and assessing unexpected working conditions in rotating machines. Such proposal performs the condition assessment under a novelty detection approach based on self-organizing maps. Thus, the proposed condition monitoring method includes the estimation of a statistical time-based set of features from acquired vibration signals; then, the data modeling is carried out through SOM and then the evaluation of novelty detection events. Finally, if novelties are detected, a retraining and incremental learning procedure is considered by including a dimensionality reduction stage by means of the linear discriminant analysis. This proposal is validated and applied to a real laboratory gearbox-based electromechanical system.
\n
\n
\n
2. Fault detection and identification
\n
The condition monitoring assessment is involved with the behavior analysis of the machine working operation; thus, the consideration of stator currents or vibrations as informative physical magnitudes for condition monitoring represents the most preferred and accepted approaches in the related literature. Also, although different information fusion levels are considered, such as signal-level or decision-level, dealing with electromechanical condition monitoring, the feature-level represents the most appropriate, since many numerical fault indicators from aforementioned physical magnitudes have been proposed as suitable fault indexes in multiple studies [9, 10]. In this regard, time-domain, frequency domain, and time-frequency domain are the three feature estimation approaches widely applied during the physical magnitude characterization process. Although techniques based on frequency and time-frequency domain, such as classical Fourier transform or wavelet analysis have been widely applied, most of these techniques require a deep knowledge of the fault effects over the resulting frequency distributions of the physical magnitudes. Indeed, as stated by Zhang et al. in [11], dealing with complex electromechanical systems, where the resulting interaction among multiple parts is reflected in the acquired physical magnitudes, the consideration of statistical time-domain features represents a performing trade-off between computational simplicity and characterization capabilities of general patterns. Such feature-level fusion scheme needs to consider the processing of a high-dimensional set of numerical features estimated during the characterization of the available physical magnitudes that, although increases the fault detection and identification capabilities, inevitably contain redundant and nonsignificant information.
\n
Dimensionality reduction procedures are applied in order to avoid low fault diagnosis performances and overfitting responses of the condition monitoring schemes. In this regard, classical dimensionality reduction techniques have been widely applied, as the principal component analysis (PCA). However, PCA aims to identify orthogonal components that maximize the preservation of the data variance. That is, PCA seeks for global data representation; thus, considering the unsupervised operation, a set of non-connected data clusters have a negative impact over the resulting representation. Other classical approaches, as linear discriminant analysis, overcome such data topology limitation by means of a supervised approach, as the LDA, where the resulting set of features is a mathematical combination of the original ones maximizing distances among classes [12, 13].
\n
Finally, the classification algorithms, play an important role in data-driven condition monitoring schemes to perform the automatic and final diagnosis outcome. In this regard, neural networks and fuzzy inferred systems classically represent the most used classifiers, but also classifiers like decision trees and support vector machines have been widely applied [14, 15, 16]. The use of these techniques, however, is related with the maximization of the classification ratio by means of the feature set decomposition following supervised training schemes. According to Shannon’s rate-distortion theory, mutual dependencies among various sources and between the input and output spaces contain the actual intrinsic dimension of the data and allow avoiding over-fitted responses. Thus, unsupervised learning approaches applied over the available feature space represent the most coherent processing procedure in order to maintain the underlying physical phenomenon of the system under monitoring. Concerning this problem, manifold learning methods have been applied in the last years to preserve the information in a lower dimensional space. Among them, the self-organizing map, SOM, is the most used, which is based on developing a neural network grid to preserve most of the original distances between feature vector representations in the original feature space [17]. Indeed, the SOM allows a high-dimensional input data mapping over a two-dimensional output layer while preserving as much as possible the structure of the input data. Although SOM leads to model the original data distribution following an unsupervised approach, each of the neuron units used during the original space characterization can be later associated with a class label; thus, through distance criteria, the diagnosis can be estimated during the assessment of a new measurement. Thus, both fault detection and identification tasks can be faced at the same time and, what is more important, considering the same criteria for both outcomes, that is, topological aspects of the data distribution.
\n
\n
\n
3. Novelty detection and diagnosis methodology
\n
The proposed condition monitoring strategy that is applied to the condition assessment of an electromechanical system under a novelty detection framework is composed of five important stages as depicted in \nFigure 1\n.
\n
Figure 1.
Rotating machinery-based electromechanical system used to demonstrate the practical implementation of the proposed method.
\n
The first stage is based on the fact that initially the machine condition is known; in this sense, it is considered as an initial condition that only the available information belongs specifically to the behavior of the healthy condition of the electromechanical system under evaluation. This assumption is asserted and taken into account since all the machinery used in most of the industrial applications starts its life cycle from an initial healthy condition, which means that all elements work properly. Therefore, under this assumption, such available information is obtained from the continuous monitoring of one vibration signal that is monitored during the working operation of the electromechanical system.
\n
In the second stage, the characterization of the machinery behavior is performed; thus, the available vibration signal is processed and analyzed, aiming to carry out a characterization of the machine working condition and also with the aim of highlighting those representative features that can represent the occurrence of abnormal operations. Precisely, the calculation of a representative set of eight statistical time-based domain features is estimated from the acquired vibration signal; this proposed set of features consists of some well-known statistical features such as mean, rms, standard deviation, variance, shape factor, crest factor, skewness, and kurtosis. Indeed, and as it has been mentioned, statistical time-based domain features provide meaningful information leading to the estimation of high-performance feature characterization due to its capability of describing trends and changes in signals; additionally, this proposed set of statistical features has been included in several condition monitoring approaches to perform the assessment of the operating working condition of electromechanical systems used in industrial application [3, 11, 16]. The corresponding mathematical equations of such numerical features are shown in \nTable 1\n.
Subsequently, in the third stage, the set of statistical features estimated from vibrations is modeled through SOM; the data modeling is performed by SOMs since this approach allows to preserve the data topology. Due to the proposed condition, monitoring strategy is based under a novelty detection framework, and the initial and available information is modeled, aiming to represent the initial known condition which is the healthy condition. As a result, a pre-defined neuron SOM grid model is first obtained to characterize the healthy condition of the electromechanical system. Then, in case additional conditions appear, the data modeling is also performed by a specific neuron SOM grid model for each one of the additional operating condition.
\n
Afterward, the novelty detection is performed in the fourth stage; in this sense, there exist different approaches for carrying out the detection of novel events. Classic novelty detection approaches are based on the evaluation of numerical threshold values, and the definition of such values depends on different criteria. Thereby, for this proposal, the novelty detection is performed by evaluating the average quantization error, Eq, obtained during the data modeling through SOMs; indeed, the novelty detection based on the Eq is a coherent option according to the data modeling to detect whether the electromechanical system condition is known or unknown. Certainly, because the healthy condition is initially the unique known and available condition, the evaluation of any other new measurement that does not belong to the known condition will exhibit a different Eq value. Thus, any change presented in the Eq value should be analyzed because this value is an important measurement related to the occurrence of unexpected and unknown events which results in the novelty detection. Otherwise, the diagnosis and condition assessment of the known conditions is carried out if any change is presented in the Eq value.
\n
Finally, the last stage is carried out in case of novelty detection; thus, this stage considers a retraining process where an incremental learning is performed with the aim of updating the available information with new data that belongs to new operating conditions. In this sense, during the detection of a novelty event, the available information that describes such novel condition is also processed, and from the acquired vibration signal, the statistical time-domain features are also estimated. Then, such new available information represented by the estimated statistical features is modeled through SOMs, and a new neuron SOM grid represents the new condition. Accordingly, as aforementioned, each new operating condition detected under this novelty detection approach has to be modeled by a specific neuron SOM model. Finally, when novelty detection occurs, such neuron SOM grids are subjected to a dimensionality reduction procedure by means of the linear discriminant analysis in order to obtain a maximum linear separation between the considered conditions and also with the aim of obtaining a visual representation the assessed conditions.
\n
\n
\n
4. Case study
\n
In order to demonstrate the practical implementation of the proposed smart monitoring based on novelty detection in an industrial application, a case of study is proposed next.
\n
A rotating machinery-based electromechanical system has been considered; such electromechanical system includes a three-phase IM of 1492-W (model WEG00236ET3E145T-W), a gearbox with 4:1 ratio (model BALDOR GCF4X01AA), and a DC used as a mechanical load (model BALDOR CDP3604). The IM is coupled shaft to shaft to the gearbox, and the gearbox is also coupled shaft to shaft to the DC generator, and a VFD (model WEGCFW08) is also used to feed and control the different operating frequencies of the IM. Besides, the DC generator is used as a non-controlled mechanical load representing around 20% of the nominal load. A picture of the second electromechanical system based on a gearbox is shown in \nFigure 2\n.
\n
Figure 2.
Rotating machinery-based electromechanical system used to demonstrate the practical implementation of the proposed method.
\n
Aiming to detect and assess the appearance of unexpected conditions, a database of different experiments is generated. The data acquisition is carried out by means of a data acquisition system (DAS) that is a proprietary low-cost design based on a field programmable gate array; such DAS uses two 12-bit 4-channel serial-output sampling analog-to-digital converters, model ADS7841 from Texas Instruments. Different physical magnitudes have been acquired during the experiments; that is, the appearance of mechanical vibrations is acquired by means of a triaxial accelerometer (LIS3L02AS4). In this regard, the accelerometer sensor is fixed on the top of the gearbox. For this proposed work, the occurrence of vibrations is analyzed because they are inherent to the rotating condition of the rotating elements that compose the electromechanical system, i.e., electric motors, gearboxes, and bearings, among others [2].
\n
The accelerometer sensor is individually mounted on a board with its corresponding signal conditioning and anti-alias filtering. During the acquisition of vibration signals, the sampling frequency is set to 3 kHz; as a result, 270 kS are stored during 90 s of continuous sampling of the working condition, in the steady-state regime, of the electromechanical system are stored. Furthermore, the IM of the experimental test bench is driven at different operating frequencies during the experimentations; specifically, the operating frequencies are set at 5, 15, and 50 Hz.
\n
During the experimentation, four different operating conditions are also evaluated: healthy (HLT), 25% of uniform wear in the gearbox (W25), 50% of uniform wear in the gearbox (W50), and 75% of uniform wear in the gearbox (W75). In this regard, the gearbox with 4:1 ratio is composed of two gears, the driver gear and the driven gear which has 18 and 72 teeth, respectively. The wear was artificially induced uniformly in all teeth of three similar driven gears: from \nFigure 3a–d\n, the set of gears tested in the gearbox-based electromechanical system. The experiments are performed by replacing iteratively the healthy gear with the damaged ones.
\n
Figure 3.
Set of the faulty conditions evaluated in the gearbox-based electromechanical system: (a) healthy gear, (b) 25% of uniform wear, (c) 50% of uniform wear, and (d) 75% of uniform wear.
\n
\n
\n
5. Competency of the method/results
\n
The proposed condition monitoring strategy is based on a novelty detection approach; the implementation of such proposal has been done in Matlab that is a sophisticated software used in several engineering applications. Indeed, the use of Matlab facilitates the signal processing for carrying out the condition assessment of the electromechanical system. Thus, the available vibration signal is first continuously monitored and acquired during the operating condition of the electromechanical systems, and then the statistical set of features is estimated from the vibration signal.
\n
As aforementioned, the initial condition belongs to the healthy condition; in this sense, the data modeling is carried out aiming to obtain a neuron SOM grid model that represents such initial condition. As a result of the data modeling, the first SOM1\n model obtained and this SOM model only characterize the healthy condition of the electromechanical system. During the data modeling, an average Eq error of 0.4932 has been obtained during the training procedure, and during the evaluation the Eq error reaches a value of 19.4419. It should be noted that during the evaluation different data information has been used; indeed, the evaluated data belong to a faulty condition tested in the gearbox. In \nFigure 4\n, a visual representation of the novelty detection achieved by the first modeled neuron SOM1\n grid is shown.
\n
Figure 4.
Novelty detection performed by SOM1 during the evaluation of the first faulty condition tested in the electromechanical system, 25% of uniform wear in the gearbox.
\n
After the first novelty detection, the process and incremental learning is carried out; in this regard, the available data that belong to the first faulty condition (25% of uniform wear) is modeled by a second neuron SOM2\n grid. Thus, the data information related to the working condition of the machine consist of two known conditions which are healthy and 25% of uniform wear. Indeed, during the training of the second SOM model, a Eq of 0.8997 is achieved during the training and during the evaluation with available data, which belongs to another unknown condition; the Eq error was 7.0773; thus, such significant increase in the Eq error depicts that an anormal condition is detected by the novelty detection approach. The visual representation of the Eq error is shown in \nFigure 5\n where it is possible to appreciate the abrupt change due to the occurrence of the unexpected faulty condition.
\n
Figure 5.
Novelty detection performed by SOM2 during the assessment of the second faulty condition, 50% of uniform wear in the gearbox.
\n
The data information to the electromechanical system condition is currently composed of three different conditions, healthy, 25%, and 50% of uniform wear in the gearbox. Later, available information related to another faulty unknown condition is evaluated after performing the retraining process and incremental learning. In this regard, during the training procedure of the third neuron SOM3 grid, the obtained Eq value was around 0.7077, and during the evaluation of the last faulty condition the achieved Eq was around 6.4367. Thus, the SOM3\n model represents the available information to the third faulty condition that is 50% of uniform wear. In \nFigure 6\n, the visual representation of the novelty detection performed is shown during the evaluation of the SOM3\n model.
\n
Figure 6.
Novelty detection carried out by SOM3 obtained for the evaluation of the third faulty condition, 75% of uniform wear in the gearbox.
\n
Subsequently, after the last retraining and incremental learning, the available information related to the faulty condition of 75% of uniform wear is also modeled by a fourth SOM model, such model is the neuron SOM4\n model, and the Eq\n error achieved during the training was 0.7700. Because four different operating conditions are detected during the operating condition of the electromechanical system, the final available information stored by the proposed novelty detection approach consist of information capable of detecting four different operating conditions. In case of more novelty detections, the retraining process and incremental learning are again performed, and the information related to the different operating conditions is updated.
\n
Finally, a visual representation of the operating conditions detected during the application of the proposed diagnosis methodology is obtained by means of applying a dimensionality reduction technique, PCA. In this sense, in \nFigure 7\n, a visual representation of the data distribution of all detected conditions is shown; in this visual representation, it is appreciated that different operating conditions appears. Indeed, different clusters appear for each detected condition because different operating frequencies were considered during the experimental evaluation of the considered conditions.
\n
Figure 7.
Resulting two-dimensional projection obtained by considering the four neuron SOM grids modeled for each one of the detected conditions.
\n
\n
\n
6. Conclusions
\n
Modern industrial production is characterized by the consideration of machine learning data-based models to support the main aspects of the manufacturing process. In this regard, two main data science challenges related with condition monitoring of electromechanical assets in the Industry 4.0 framework are (i) the premise that only information of the healthy condition is initially available and (ii) the adaptation of the fault detection and identification scheme in order to incorporate new operating conditions. Thus, this paper proposes a new methodology for multi-fault detection and identification based on incremental learning applied to novel fault detection on electromechanical systems by analyzing vibrations and stator current signatures of the electric motor drive.
\n
Moreover, the proposed condition monitoring strategy based on a novelty detection approach is capable of being applied to other electromechanical systems, and also the consideration of other different physical magnitudes can be also included in such proposal.
\n
\n
Acknowledgments
\n
This research work has been partially supported by the FOFIUAQ2018 under the registered project FIN201811.
\n
Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"smart monitoring, condition assessment, novelty detection, artificial intelligence, Industry 4.0, rotating machinery",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/69804.pdf",chapterXML:"https://mts.intechopen.com/source/xml/69804.xml",downloadPdfUrl:"/chapter/pdf-download/69804",previewPdfUrl:"/chapter/pdf-preview/69804",totalDownloads:433,totalViews:0,totalCrossrefCites:0,totalDimensionsCites:2,totalAltmetricsMentions:0,impactScore:1,impactScorePercentile:61,impactScoreQuartile:3,hasAltmetrics:0,dateSubmitted:"May 15th 2019",dateReviewed:"September 27th 2019",datePrePublished:null,datePublished:"March 25th 2020",dateFinished:"October 29th 2019",readingETA:"0",abstract:"The application of condition monitoring strategies for detecting and assessing unexpected events during the operation of rotating machines is still nowadays the most important equipment used in industrial processes; thus, their appropriate working condition must be ensured, aiming to avoid unexpected breakdowns that could represent important economical loses. In this regard, smart monitoring approaches are currently playing an important role for the condition assessment of industrial machinery. Hence, in this work an application is presented based on a novelty detection approach and artificial intelligence techniques for monitoring and assessing the working condition of gearbox-based machinery used in processes of the Industry 4.0. The main contribution of this work lies in modeling the normal working condition of such gearbox-based industrial process and then identifying the occurrence of faulty conditions under a novelty detection framework.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/69804",risUrl:"/chapter/ris/69804",book:{id:"9428",slug:"new-trends-in-the-use-of-artificial-intelligence-for-the-industry-4-0"},signatures:"Saucedo-Dorantes Juan Jose, Jaen-Cuellar Arturo Yosimar and Elvira-Ortiz David Alejandro",authors:[{id:"305195",title:"Dr.",name:"Juan Jose",middleName:null,surname:"Saucedo-Dorantes",fullName:"Juan Jose Saucedo-Dorantes",slug:"juan-jose-saucedo-dorantes",email:"jsaucedo@hspdigital.org",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"305305",title:"Dr.",name:"Arturo Yosimar",middleName:null,surname:"Jaen-Cuellar",fullName:"Arturo Yosimar Jaen-Cuellar",slug:"arturo-yosimar-jaen-cuellar",email:"ayjaen@hspdigital.org",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}},{id:"305315",title:"Dr.",name:"David Alejandro",middleName:null,surname:"Elvira-Ortiz",fullName:"David Alejandro Elvira-Ortiz",slug:"david-alejandro-elvira-ortiz",email:"delvira@hspdigital.org",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Fault detection and identification",level:"1"},{id:"sec_3",title:"3. Novelty detection and diagnosis methodology",level:"1"},{id:"sec_4",title:"4. Case study",level:"1"},{id:"sec_5",title:"5. Competency of the method/results",level:"1"},{id:"sec_6",title:"6. Conclusions",level:"1"},{id:"sec_7",title:"Acknowledgments",level:"1"},{id:"sec_10",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'\nFrank AG, Dalenogare LS, Ayala NF. Industry 4.0 technologies: Implementation patterns in manufacturing companies. International Journal of Production Economics. 2019;210:15-26\n'},{id:"B2",body:'\nContreras-Hernandez JL, Almanza-Ojeda DL, Ledesma S, Ibarra-Manzano MA. Motor fault detection using quaternion signal analysis on FPGA. Measurement. 2019;138:416-424\n'},{id:"B3",body:'\nSaucedo-Dorantes JJ, Osornio-Rios RA, Delgado-Prieto M, Romero-Troncoso RJ. Diagnosis methodology based on statistical-time features and linear discriminant analysis applied to induction motors. IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED). 2017;2017:517-523. DOI: 10.1109/DEMPED.2017.8062404\n'},{id:"B4",body:'\nRibeiro DA, Ferreira DD, Pereira DA, Braga Junior RA, Nunes RD. Mechanical fault detection in electric motors measured by a digital signal processing device in an optical mouse. Measurement. 2019;138:350-355\n'},{id:"B5",body:'\nFrini M, Soualhi A, El Badaoui M. Gear faults diagnosis based on the geometric indicators of electrical signals in three-phase induction motors. Mechanism and Machine Theory. 2019;138:1-15\n'},{id:"B6",body:'\nZhao Y, Li T, Zhang X, Zhang C. Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future. Renewable and Sustainable Energy Reviews. 2019;109:85-101. DOI: 10.1016/j.rser.2019.04.021\n'},{id:"B7",body:'\nNasiri S, Khosravani MR, Weinberg K. Fracture mechanics and mechanical fault detection by artificial intelligence methods: A review. Engineering Failure Analysis. 2017;81:270-293. DOI: 10.1016/j.engfailanal.2017.07.011\n'},{id:"B8",body:'\nLiu R, Yang B, Zio E, Chen X. Artificial intelligence for fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing. 2018;108:33-47. DOI: 10.1016/j.ymssp.2018.02.016\n'},{id:"B9",body:'\nKang Md. M, Islam R, Kim J, Kim JM, Pecht M. Hybrid feature selection scheme for reducing diagnostic performance deterioration caused by outliers in data-driven diagnostics. IEEE Transactions on Industrial Electronics. 2016;63(5):3299-3310\n'},{id:"B10",body:'\nWang S, Yub J, Lapirac E, Lee J. A modified support vector data description based novelty detection approach for machinery components. Applied Soft Computing. 2013;13:1193-1205. DOI: 10.1016/j.asoc.2012.11.005\n'},{id:"B11",body:'\nZhang X, Chei W, Wang B, Chen X. Intelligent fault diagnosis of rotating machinery using support vector machine with ant colony algorithm for synchronous feature selection and parameter optimization. Neurocomputing. 2015;167:260-279\n'},{id:"B12",body:'\nHe Q, Ding X, Pan Y. Machine fault classification based on local discriminant bases and locality preserving projections. Mathematical Problems in Engineering. 2014;2014:1-12. Article ID: 923424\n'},{id:"B13",body:'\nPrieto-Moreno A, Llantes-Santiago o, Garcia-Moreno E. Principal components selection for dimensionality reduction using discriminant information applied to fault diagnosis. Journal of Process Control. 2015;33:12-24\n'},{id:"B14",body:'\nCunha-Palacios RH, Goedtel A, Godoy WF, Fabri JA. Fault identification in the stator winding of induction motors using PCA with artificial neural networks. Journal of Control, Automation and Electrical Systems. 2016;27(4):406-418\n'},{id:"B15",body:'\nManimala K, Getzy David DI, Selvi K. A novel data selection technique using fuzzy c-means clustering to enhance SVM-based power quality classification. Soft Computing. 2015;19(5):3123-3144\n'},{id:"B16",body:'\nSadooghi MS, Khadem SE. Improving one class support vector machine novelty detection scheme using nonlinear features. Pattern Recognition. 2018;83:14-33\n'},{id:"B17",body:'\nLi W, Zhang S, He G. Semisupervised distance-preserving self-organizing map for machine-defect detection and classification. IEEE Transactions on Instrumentation and Measurement. 2013;62(5):869-879\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Saucedo-Dorantes Juan Jose",address:"jsaucedo@hspdigital.org",affiliation:'
HSPdigital CA-Mecatronica, Engineering Faculty, Autonomous University of Queretaro, Mexico
HSPdigital CA-Mecatronica, Engineering Faculty, Autonomous University of Queretaro, Mexico
'},{corresp:null,contributorFullName:"Elvira-Ortiz David Alejandro",address:null,affiliation:'
HSPdigital CA-Mecatronica, Engineering Faculty, Autonomous University of Queretaro, Mexico
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1. Introduction
Frozen shoulder, or adhesive capsulitis, is a condition caused by impaired soft tissues and the articular capsule of the shoulder. Primary frozen shoulder is common, and it is characterized by debilitating conditions. The prevalence is between 2% and 5% that increasing to 10–38% in patients with diabetes and thyroid disease. The age of patients is commonly between 40 and 65 years old, and the incidence appears higher in females than males [1, 2, 3]. It may also occur after trauma or in association with other joint diseases, as acromioclavicular osteoarthritis, which is referred to as a secondary frozen shoulder [4].
2. Frozen shoulder: clinical definition
Codman defined frozen shoulder as a clinical condition that can hardly be defined, it is complicated to enclose it in a single pathological mechanism, and therefore, even less easy to define its treatment. Instead, the term “adhesive capsulitis” was introduced by Neviaser [5] to describe a tissue inflammation condition and subsequent fibrosis involving the articular capsule of the shoulder. In addition, the definition of “frozen” shoulder refers to pain and immobility correlation. Lack of function causes the capsule to thicken, making it even more difficult to move. Therefore the functional expression of pathology defines the term “frozen.”
Frozen shoulder is characterized by an insidious and progressive loss of active and passive mobility in the glenohumeral joint presumably due to capsular contracture.
Frozen shoulder can be classified as primary or secondary. Primary idiopathic frozen shoulder can be often associated with other diseases, such as diabetes mellitus, thyroid diseases, and Parkinson’s disease. Secondary adhesive capsulitis can occur after trauma or immobilization. Frozen shoulder is estimated to affect 2–5% of the general population. A patient who experiments with this pathology can be significantly painful and disabling for some months. It most commonly affects those in their fourth to sixth decades of life and more often occurs in women than in men [6].
3. Frozen shoulder: pathophysiology
The pathophysiological mechanism underlying the pathology remains poorly understood. Scientific literature shows a correlation with elevated serum cytokine levels [7]. Although the precise pathophysiology remains unclear, recent evidence identifies elevated serum levels of cytokines as part of the process. Cytokines are polypeptide, non-antigen-specific mediators that act as communication signals between immune system cells and between them and different organs and tissues. Elevated cytokine levels appear predominantly involved in the cellular mechanisms of inflammation and fibrosis sustained in the primary and some secondary frozen shoulder. Bunker et al. [8] defined that a mild lesional event would trigger an inflammatory response that results in excessive production of fibroblasts, which release type I and type III collagen. Fibroblasts differentiate into myofibroblasts, causing the newly deposited type III collagen to contract. This would result in an imbalance between the inflammatory phase and the remodeling underlying the fibrosis.
Rodeo et al. [9] described pathological processes like inflammation and fibrosis: synovial hyperplasia determines a decrease of vascularity. This phenomenon leads to fibrosis in the sub-synovium and synovium of capsular tissue. This condition could be the expression of an immune response [10]. Other studies have shown that frozen shoulder is associated with a dense collagen matrix containing fibroblasts and myofibroblasts, suggestive of a fibrotic process [9, 11, 12, 13]. Furthermore, the component of the immune system that is activated is represented by B lymphocytes, mast cells, and macrophages. Several studies have suggested the immune response overlaps with inflammatory synovitis, leading to capsular fibrosis in the later stages [5, 14].
There are many etiopathological hypotheses, and all studies suggest that both inflammation and fibrosis of the joint capsule are regulated by cytokines, growth factors, MMPs, and immune cells. The results of the next studies will provide the control mechanisms of FS and identify new therapeutic targets to identify its treatment [14, 15, 16].
4. Frozen shoulder: symptoms
Patients typically demonstrate a characteristic history, clinical presentation, and recovery. Clinical syndromes include pain, a limited range of motion (ROM), and muscle weakness from disuse [17].
The pain has a typical course involving the entire shoulder up to the insertion of the deltoid muscle. The patient reports difficulty sleeping on the affected side and difficulty in active movement. Clinical examination shows atrophy of the spinate, restriction on passive mobilization, with painful and limited elevation and external rotation.
Pain is localized in the shoulder (in the deltoid region), sometimes in the arm with functional limitation. In patients who have been in pain for a long time, may present medial to the scapula. This happens because incorrect movements of the scapulothoracic are established to compensate for the limitation of the glenohumeral joint [18].
Neviaser et al. [19] elaborated on the natural history of frozen shoulder and distinguished the following stages:
Stage 1: It is defined as a pre-adhesive stage. It is characterized by erythematous joint inflammation and mild pain in the most extreme degrees of movement. It is often misunderstood because it has a similar clinical presentation to the impingement of the rotator cuff.
Stage 2. It is the acute–adhesive stage. Patients complain of severe pain up to almost the last degree of movement of the joint. An inflammatory process with thickening of the synovium and change of connective tissue is highlighted.
Stage 3. Fibrotic or “frozen” stage. At this stage, fibrosis is characteristic of the presence of more mature adhesions. The pain becomes less intense and joint stiffness becomes prevalent.
Stage 4. At this stage, the restriction on movement remains but without synovitis. In fact, it is defined as the “thawing” phase. Patients present painless stiffness and movement typically improves by remodeling (Figure 1).
Figure 1.
Natural history of frozen shoulder.
5. Diagnosis
5.1 Clinical diagnosis
Primary frozen shoulder is essentially a clinical diagnosis. Frozen shoulder is characterized by an insidious and progressive loss of active and passive mobility in the glenohumeral joint presumably due to capsular contracture. Patients typically demonstrate a characteristic history, clinical presentation, and recovery. Clinical syndromes include pain, a limited range of motion (ROM), and muscle weakness from disuse [20]. To carry out the clinical examination of the shoulder it is necessary to observe the neck and evaluate through a functional examination if the pain comes from the cervical spine. Subsequently, following the standard shoulder examination protocol, it is necessary to proceed with the inspection of the shoulder. Observe if there are scars, reduced Tropism of rotator cuff/deltoid, bone landmarks, and spinal and scapular alignment. People with frozen shoulders have a limited range of both active and passive motion. Next, proceed to palpation to rule out acromioclavicular-induced pain. Following this, proceed with an assessment of shoulder range of motion (ROM). There are four movements that are useful in the examination—flexion, abduction, internal rotation, and external rotation. Flexion , abduction and internal rotation are evaluated with active and passive mobilization, while external rotation is evaluated only with passive mobilization [21].
Shoulder pain appears slowly and radiates to the insertion of the deltoid. The patient reports inability to sleep on the affected side, limitation to active movement, and painful elevation of the shoulder. Progressively atrophy of the spinate appears.
Imaging studies are not necessary for the diagnosis of adhesive shoulder capsulitis but may be helpful to rule out other causes of a painful and stiff shoulder. Usually, resistance in the last degrees of movement is described, this sensation is defined as firm and “leathery.” During the examination the pain is prevalent, the patient cannot get to the point where even the examiner would feel the resistance. Therefore it is most frequently described as a feeling of “empty” end [22].
5.2 Evaluation scale
It should be used to validate functional outcome measures, such as the disabilities of the arm, shoulder, and hand (DASH), the American Shoulder and Elbow Surgeons shoulder scale (ASES), or the Shoulder Pain and Disability Index (SPADI). The DASH questionnaire consists of 30 questions that inquire about symptoms and functions of the upper limbs.
Table 1 describes the 30 items that are carried out with the application of the scale. DASH investigates the severity of pain, activity-related pain, tingling, weakness, and stiffness (five items), and the effect of the upper limb problem on social activities, work, sleep, and self-image (four items). These provide a single main score, the DASH function/symptoms (DASH-FS) score, which is basically a summation of the responses on a one-to-five scale, after transformation to a zero (no disability) to 100 (severe disability) scale [23].
Dash questionnaire
Difficulty
No
Mild
Moderate
Severe
Unable
1. Open a tight or new jar
1
2
3
4
5
2. Write
1
2
3
4
5
3. Turn a key
1
2
3
4
5
4. Prepare a meal
1
2
3
4
5
5. Push open a heavy door
1
2
3
4
5
6. Place an object on a shelf above your head
1
2
3
4
5
7. Do heavy household chores (e.g., wash walls, wash floors)
1
2
3
4
5
8. Garden or do yard work
1
2
3
4
5
9. Make a bed
1
2
3
4
5
10. Carry a shopping bag or briefcase
1
2
3
4
5
11. Carry a heavy object (over 10 lbs)
1
2
3
4
5
12. Change a light bulb overhead
1
2
3
4
5
13. Wash or blow-dry your hair
1
2
3
4
5
14. Wash your back
1
2
3
4
5
15. Put on a pullover sweater
1
2
3
4
5
16. Use a knife to cut food
1
2
3
4
5
17. Recreational activities that require little effort (e.g., card playing, knitting, etc.)
1
2
3
4
5
18. Recreational activities in which you take some force or impact through your arm, shoulder, or hand (e.g., golf, hammering, tennis, etc.)
1
2
3
4
5
19. Recreational activities in which you move your arm freely (e.g., playing frisbee, badminton, etc.)
1
2
3
4
5
20. Manage transportation needs (getting from one place to another)
1
2
3
4
5
21. Sexual activities
1
2
3
4
5
Disabilities
Not at all
Slightly
Moderately
Quite a bit
Extremely
22. During the past week, to what extent has your arm, shoulder, or hand problem interfered with your normal social activities with family, friends, neighbors, or groups?
1
2
3
4
5
Not limited at all
Slightly limited
Moderately limited
Very limited
Unable
23. During the past week, were you limited in your work or other regular daily activities as a result of your arm, shoulder, or hand problem?
1
2
3
4
5
Severity symptoms in the last week
None
Mild
Moderate
Severe
Extreme
24. Arm, shoulder, or hand pain.
1
2
3
4
5
25. Arm, shoulder. or hand pain when you performed any specific activity
1
2
3
4
5
26. Tingling (pins and needles) in your arm, shoulder, or hand
1
2
3
4
5
27. Weakness in your arm, shoulder, or hand
1
2
3
4
5
28. Stiffness in your arm, shoulder, or hand
1
2
3
4
5
Difficulty
No
Mild
Moderate
Severe
So much- I cannot sleep
29. During the past week, how much difficulty have you had sleeping because of the pain in your arm, shoulder, or hand?
1
2
3
4
5
Strongly disagree
Disagree
Neither agree nor disagree
Agree
Strongly agree
30. I feel less capable, less confident, or less useful because of my arm, shoulder, or hand problem
1
2
3
4
5
Work module (optional)
This part of the scale is about the impact on working skills (including doing housework). These are four questions that concern five levels of difficulty in using the shoulder during work activities
Sports/performing arts module (optional)
This part of the scale is about the impact of playing musical instruments or sport or both. These are four questions that concern five levels of difficulty in using the shoulder during work activities
Table 1.
Disabilities of the arm, shoulder, and hand (DASH).
The shoulder and elbow surgeons shoulder scale (ASES) is a physician assessment section that includes physical examination and documentation of a range of motion, strength, and instability, and demonstration of specific physical signs. No score is derived for this section of the instrument. The patient self-evaluation section has 11 items that can be used to generate a score. These are divided into two areas—pain (one item) and function (10 items) (Table 2).
Shoulder and elbow surgeons shoulder scale (ASES)
1. Usual work
2. Usual sport/leisure activity?
3. Do you have shoulder pain at night?
Yes
No
4. Do you take pain killers, such as paracetamol (acetaminophen), diclofenac, or ibuprofen?
Yes
No
5. Do you take strong pain killers, such as codeine, tramadol, or morphine?
Yes
No
6. How many pills do you take on an average day?
7. Intensity of pain?
Visual analog scales (VAS)—from 10 (pain as bad as it can be) to 0 (No pain at all)
8. Is it difficult for you to put on a coat?
Unable to do
Very difficult to do
Somewhat difficult
Not difficult
9. Is it difficult for you to sleep on the affected side?
Unable to do
Very difficult to do
Somewhat difficult
Not difficult
10. Is it difficult for you to wash your back/do up bra?
Unable to do
Very difficult to do
Somewhat difficult
Not difficult
11. Is it difficult for you to manage toileting?
Unable to do
Very difficult to do
Somewhat difficult
Not difficult
Table 2.
Shoulder and elbow surgeons shoulder scale (ASES).
The final score is tabulated by multiplying the pain score (maximum 10) by 5 (therefore total possible 50) and the cumulative activity score (maximum 30) by 5/3 (therefore, a total possible 50) for a total of 100 [24].
The Shoulder Pain and Disability Index (SPADI) is a self-administered questionnaire that consists of two dimensions, one for pain and the other for functional activities. The pain dimension consists of five questions regarding the severity of an individual’s pain [25].
5.3 Diagnostic imaging
Radiographic examination is carried out to make the differential diagnosis and exclude other pathologies, for example, calcific tendinitis, rupture of the rotator cuff, arthritis of the glenohumeral, and acromioclavicular joint or a neoplastic process. In patients with frozen shoulder radiographic examination is normal, however, osteopenia of the humerus head may be an indirect sign [26].
Ultrasound is an essential tool for diagnosing shoulder disorders. However, the role of ultrasound in assessing and diagnosing adhesive capsulitis has not been fully studied. Sonography had high diagnostic accuracy for the diagnosis of adhesive capsulitis using a combination of parameters, such as coracohumeral ligament (CHL) thickness, rotator interval (RI) thickness, and hypervascularity, axillary recess (AR) thickness [27, 28].
Several studies have shown that the CHL is thickened and stiffened in adhesive capsulitis on ultrasound [29, 30, 31] .Other researches correlate AR thickening as a key diagnostic finding of adhesive capsulitis [32] and approximately the AR cutoff value for adhesive capsulitis diagnosis was 4 mm [28].
RI vascularity is a sign of adhesive capsulitis, but controversy remains in the literature about hypervascularity of the RI in adhesive capsulitis [33].
Magnetic resonance imaging (MRI) and magnetic resonance angiography (MRA) may reveal thickening of capsular and pericapsular tissues as well as a contracted glenohumeral joint space. Sliding movement of the supraspinatus tendon [34].
Arthrography is rarely indicated in the diagnosis of frozen shoulder syndrome. It is an invasive procedure that is painful and costly and does not necessarily provide diagnostic insight but it may be associated with a therapeutic articular injection of corticosteroids as a therapeutic intervention [35].
6. Treatments
The goal of the treatment of adhesive capsulitis is to restore the shoulder to a painless and functional joint [36, 37].
6.1 Pharmacological treatment
Initial treatment is aimed at reducing inflammation and pain. Analgesic and anti-inflammatory drugs are used. Aspirin and paracetamol are the most used and with fewer side effects, the dosage is similar to that used in osteoarthritis. Among the nonsteroidal anti-inflammatory drugs (NSAIDs) the most commonly used are ibuprofen, which has the lowest incidence of side effects, naproxen, and diclofenac [38].
Corticosteroids (for which the generic term “steroids” is usually used) strongly suppress all stages of acute and chronic inflammation. In relation to frozen shoulders, they may be injected intra-articularly (directly into the joint) or taken orally. Intra-articular injections of corticosteroids are the most used method. Corticosteroid intra-articular injections demonstrate short-term (4–6 weeks) benefits. Literature reported a moderate effect of corticosteroid injections on pain, external rotation ROM, and disability at 6 weeks, and only small effects after 12 weeks [39, 40]. Corticosteroid injections have been shown to be as effective as exercise for treating frozen shoulder, particularly when provided in the early stages of the pathology. Blanchard et al. [41] suggested that corticosteroid injections have a greater effect when compared to physical therapy when utilized within the first 6 weeks of treatment, although these differences diminished over time.
The injection of sodium hyaluronate (defined as distension or hydrodilation therapy) into the glenohumeral joint for the treatment of adhesive capsulitis results in an improvement in pain and range of motion, similar to the effects of corticosteroid injection but with fewer side effects. Hyaluronic acid has anti-inflammatory properties and it is similar the synovial fluid that occurs naturally in the joints. It works by acting like a lubricant and shock absorber in the joints and helps the joints to work properly [42].
These lubricating effects of hyaluronate have led to use in orthopedic surgery as well, via prevention of adhesion formation after both wrist and finger flexor tendon repair [43]. Thus, extrapolation to the treatment of stiff shoulder and adhesive capsulitis has demonstrated success and improvements in range of motion, pain, and function.
6.1.1 Physical therapy
6.1.1.1 Ultrasound
A common clinical practice among physical therapists is the use of ultrasound prior to capsular and soft tissue stretching techniques based upon its thermal and mechanical effects. Ultrasound is used to manage several soft-tissue conditions, such as tendinitis, bursitis, and muscle spasm; reabsorb calcium deposits in soft tissue; and reduce joint contractures, pain, and scar tissue. Used in conjunction with hot packs, muscle spasms and muscle guarding may be reduced [44]. The effect of ultrasound therapy at a frequency of 1 MHz, unlike the hot pack that produces surface heating, is a heating in the deeper tissues due to the increase in blood flow resulting in an analgesic muscle relaxant effect and wash out of pain mediators. Ultrasound therapy is used in association with the electric current that produces a modulation of muscle tone or further modulates pain. Robertson et al. [45] reported the usage of ultrasound therapy (UST) clinically in the rehabilitation of patients with frozen shoulders. Direct contact is the most common method that therapist applies ultrasound. It consists in the application of a transducer that is pressed gently into conductive gel and against the skin.
It is recommended that ultrasound be applied in a pulsed mode at low intensity (0.5–1.0 W/cm2) during the acute phase of inflammation to minimize the risk of aggravating the condition and to accelerate recovery, and that continuous ultrasound at high enough intensity to increase tissue temperature be applied in combination with stretching to assist in the resolution of chronic phase, only if the problem is accompanied by soft tissue shortening [46, 47, 48]. In a guideline it is reported that therapeutic ultrasound (US) was effective in the treatment of calcific tendonitis of the shoulder, there was no evidence that it was beneficial for other forms of shoulder pain (e.g., capsulitis, bursitis, tendonitis) [49].
The use of ultrasound therapy is indicated as a treatment for the painful phase of adhesive capsulitis and is indicated in the literature alone or in therapy with other therapies (stretching, mobilization, transcutaneous electrotherapy, and laser therapy) with a type B degree of evidence (there is research-based evidence to support the recommendation) [50]. Other studies have shown efficacy not superior to other therapies [51].
TENS consists of low-frequency electrical pulses (generated by a small, portable unit) transmitted to the tissues through electrodes on the skin. The pulses stimulate peripheral nerves in such a way as to suppress the perception of pain. TENS therapy determines analgesia by different mechanisms—by causing interactions between types of nerve fibers, resulting in a “block” on the transmission of pain signals to the brain; or by releasing hormones that block pain receptors in the central nervous system. The effects of transcutaneous electrical nerve stimulation (TENS) for the treatment of adhesive capsulitis seem to be superior in comparison to stretching exercise [52].
6.1.1.3 Electromagnetic therapy
Electromagnetic fields (EMFs) provide a noninvasive, safe, and easy method to treat pain with respect to musculoskeletal diseases. Magnetic field therapy was applied to promote bone healing, treat osteoarthritis and inflammatory diseases of the musculoskeletal system, alleviate pain, enhance healing of ulcers, and reduce spasticity [53, 54]. This mechanism could promote the resolution of pain by accelerating the removal of inflammatory substances. PEMF stimulates chondrocyte proliferation, differentiation, and extracellular matrix synthesis through the release of anabolic morphogens, such as bone morphogenetic proteins and anti-inflammatory cytokines [55].
Pulsed electromagnetic field (PEMF) therapy has been reported to produce anti-inflammatory and bone-healing effects, but it is unclear whether—it is more or less effective than placebo, or whether other electrotherapy modalities are an effective adjunct to exercise for the treatment of frozen shoulder.
6.1.1.4 Extracorporeal shock wave therapy (ESWT)
ESWT has been recently receiving attention for the treatment of the frozen shoulder. Extracorporeal shock waves therapy (ESWT) represents a valid tool for a wide range of disorders, both in orthopedics and rehabilitative medicine (tendon pathologies, bone healing disturbances, vascular bone diseases), but also in dermatology and vulnology (wound healing disturbances, ulcers, painful scars), neurology (spastic hypertonia and related disturbances), some andrologic disturbances (induratio penis plastica and erectyle disfunctions), and cardiology (in relation to ischemic heart diseases) [56]. ESWT is a treatment method that applies extracorporeal shock waves to lesions to aid revascularization and stimulate or reactivate the healing of bones and connective tissues such as tendons, thereby relieving pain and improving functions. Data suggest that in the field of tendinopathies ESWT can be considered not only as a symptomatologic therapy but rather a real curative treatment, able to relieve pain and inflammation in the short-medium term but also to positively interfere with tendon structure in a regenerative way [57]. In doing this, it causes changes in cells’ metabolism and the permeability of endothelial cell tissues, leading to pain relief and having positive effects on soft tissues [58]. A recent systematic review demonstrated the effectiveness and safety of ESWT for frozen shoulder; ESWT determines the reduction of pain intensity, and it improves shoulder function, quality of life without adverse events [59].
6.2 Physiotherapy
Several studies have examined the effect of joint mobilization in patients with adhesive capsulitis, and although there is evidence that it may be beneficial, there is little evidence to support superior efficacy over other interventions [60, 61, 62].
Joint mobilization procedures are primarily directed to the glenohumeral joint to reduce pain and increase motion and function in patients with adhesive capsulitis. Mobilization techniques improve the normal extensibility of the shoulder capsule and stretch the tightened soft tissues to induce beneficial effects. Mulligan’s mobilization-with-movement (MWM) treatment techniques, could be used. The most important points of the Mulligan Concept include the active participation of the patient and the elimination of pain during therapy [63]. A recent review of the literature analyzed 16 controlled clinical trial (CCT) or randomized controlled trial (RCT) studies that used MWMs demonstrating efficacy on pain and disability [64].
Also, stretching exercises appear to influence pain and improve ROM. The Harvard Special Health Report offers some stretching exercises that are effective in the treatment of adhesive capsulitis—pendulum stretch, towel stretch finger walk, cross-body reach, armpit stretch, starting to strengthen, outward rotation, inward rotation. These exercises can be performed with the physiotherapist or carried out as a home program [65].
No evidence exists to guide the optimal frequency, number of repetitions, or duration of stretching exercises. Stretching beyond painful limits may result in poorer outcomes. Therefore, stretching intensity that matches the given level of tissue irritability is indicated.
6.3 Manual myofascial therapy
Manual therapy may include myofascial work to release abnormal tension and restore mobility and function and identify fascial restrictions using motion testing and palpation.
In the myofascial treatment could be used simple techniques for muscle treatment and joint manipulations, such as:
lateral elongation, a force applied with a right angle to the longitudinal axis of the muscle fascicles.
linear extension and removal of the insertion points, with force applied proximally and distally to a dysfunctional area or muscle insertions, with longitudinal and parallel direction to the muscle fascicles.
linear shortening and rapprochement of insertion points, with force applied proximally and distally to a dysfunctional area or muscle insertions, with longitudinal and parallel direction to the muscle fascicles.
deep pressure, constant pressure on a zone of retraction or muscle tension or close to its bony insertion [66].
6.4 Minimally invasive treatments
6.4.1 Acupuncture
Acupuncture can be used to treat the pain of the frozen shoulder. It involves inserting needles into the skin at sites that vary from case to case and also depend on the practitioners’ school of thought. Traditional Chinese medicine regarded acupuncture as an effective measure in aborting the signs and symptoms of frozen shoulder and in preventing future recurrence.
In the treatment of frozen shoulder, as in many other diseases, one in long, 30 gauge, disposable, sterilized, filiform needles are usually used. The sides of the application are defined as local points and distal points [67].
An integration approach can be ear acupuncture in the treatment of the frozen shoulder. According to traditional Chinese medicine, the sensitive spots on the auricle are anatomically and pathologically related to the affected shoulder joint [68] (Figure 2).
Figure 2.
Ear acupuncture in the treatment of frozen shoulder.
6.4.1.1 Kinesio taping (KT)
Kinesio taping is a complementary therapy based on the application of an elastic membrane that allows relieving pain. The effect on pain is pain modulation through pain gate control theory. The epidermis is equipped with a series of nerve receptors that, if subjected to a series of external stimuli, communicate with the underlying muscles. As a result, depending on how they are placed, the tapes can inhibit a contracted muscle or facilitate lymphatic flow, decreasing pain and inflammation [69]. The application KT can produce local physiological changes that resulted in therapeutic effects, such as the relief of pain (pain gate mechanism, reducing muscle spasm) and improvement in ROM (tissue extensibility) [70, 71].
6.5 Operative treatments
6.5.1 Arthroscopic capsular release and manipulation under anesthetic (MUA)
Arthroscopic treatment is usually indicated in patients who do not respond to drug and/or rehabilitation therapy. Usually, during this procedure, a manipulation under anesthetic (MUA) is carried out as in this way, it is possible to reduce the potential damage by allowing it to be performed with less force. In addition to a general anesthetic, it is normal for a regional nerve block to be given. This causes postoperative numbness and enables the patient to get moving at the earliest possible stage. Intensive physiotherapy is regarded as essential to a good outcome.
6.6 Postural educational program
After a period of unconditioning typical of the acute phase of pain and contracture, it is necessary to learn again the correct body schema and achieve the complete recovery of postural control. It is possible through a progressive recovery of good motor control, thanks to the muscular selective reinforcement with the increasing development of strength in different patterns of movement, both the proprioceptive recovery. In the last rehabilitation phase it is necessary to restore the sensorimotor skills including proprioception static and dynamic balance either with aquatic rehabilitation therapy or through platform swing walkway, which is a common way to improve gait pattern through activation of sensory stimuli (visual, auditory, vestibular, and somatosensory) [72, 73].
Physical therapy Injection therapy Medication therapy Mobilization and manual therapy
2–4 weeks
Full passive range of motion should be achieved by 2–4 weeks Scapular and glenohumeral joint mobilization Begin rotator cuff retraining and strengthening, focus on restoring proper biomechanics
4–8 weeks
Progressive strength training contingent upon perfect biomechanics Development of independent home and gym program (aquatic microgravity environment)
8–10 weeks
Progression into normal activity and exercise program Long home maintenance program to include daily ROM exercises, rotator cuff program, and postural educational program
Table 3.
Summary of therapeutic strategies.
7. Conclusion
Often a rehabilitative success is defined by the return of normal motion rather than pain-free functional motion, but adhesive capsulitis is a challenging condition for both the physical therapist and patient. In fact, the healing process takes months to restore full mobility without pain, considering the presence of dense fibrotic tissue and the months of collagen remodeling required to recover soft tissue length. The rehabilitation of frozen shoulder is frequently prolonged despite multiple therapeutic methods because of the difficulty of acting on the degenerative process of the cartilage matrix and the progress of adhesive capsulitis. It is important to the diagnosis process and assessment to choose the best intervention or a combination of strategies for each patient. Although in scientific literature, a definition of the best rehabilitation approach is still needed, following an integrated, multifaceted, and combination of evidence-based approaches, therapeutic success can be achieved!
Disclosure
The author reports no conflicts of interest in this work.
\n',keywords:"shoulder, rehabilitation, pain, physical therapy, complementary therapy, physiotherapy",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/80359.pdf",chapterXML:"https://mts.intechopen.com/source/xml/80359.xml",downloadPdfUrl:"/chapter/pdf-download/80359",previewPdfUrl:"/chapter/pdf-preview/80359",totalDownloads:75,totalViews:0,totalCrossrefCites:0,dateSubmitted:"December 3rd 2021",dateReviewed:"December 20th 2021",datePrePublished:"February 4th 2022",datePublished:null,dateFinished:"February 4th 2022",readingETA:"0",abstract:"Frozen shoulder, or adhesive capsulitis, is a condition caused by impaired soft tissues and the articular capsule of the shoulder. Although the precise etiology remains unclear, recent evidence identifies elevated serum cytokine levels as part of the process. It is characterized by an insidious and progressive loss of active and passive mobility in the glenohumeral joint presumably due to capsular contracture. Several treatments are recognized and utilized to reduce pain and improve range-of-motion faster than the disease’s natural history course. The chapter aims to spread knowledge about this often-misunderstood pathology and to highlight the role of the rehabilitative therapeutic approach.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/80359",risUrl:"/chapter/ris/80359",signatures:"Simona Maria Carmignano",book:{id:"11243",type:"book",title:"Shoulder Surgery for RC Pathology, Arthropathy and Tumors",subtitle:null,fullTitle:"Shoulder Surgery for RC Pathology, Arthropathy and Tumors",slug:null,publishedDate:null,bookSignature:"Dr. Dimitrios D. Nikolopoulos and Dr. George K. Safos",coverURL:"https://cdn.intechopen.com/books/images_new/11243.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-80355-397-9",printIsbn:"978-1-80355-396-2",pdfIsbn:"978-1-80355-398-6",isAvailableForWebshopOrdering:!0,editors:[{id:"228477",title:"Dr.",name:"Dimitrios D.",middleName:null,surname:"Nikolopoulos",slug:"dimitrios-d.-nikolopoulos",fullName:"Dimitrios D. Nikolopoulos"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"206445",title:"Dr.",name:"Simona Maria",middleName:null,surname:"Carmignano",fullName:"Simona Maria Carmignano",slug:"simona-maria-carmignano",email:"simona.carmignano@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Frozen shoulder: clinical definition",level:"1"},{id:"sec_3",title:"3. Frozen shoulder: pathophysiology",level:"1"},{id:"sec_4",title:"4. Frozen shoulder: symptoms",level:"1"},{id:"sec_5",title:"5. Diagnosis",level:"1"},{id:"sec_5_2",title:"5.1 Clinical diagnosis",level:"2"},{id:"sec_6_2",title:"5.2 Evaluation scale",level:"2"},{id:"sec_7_2",title:"5.3 Diagnostic imaging",level:"2"},{id:"sec_9",title:"6. Treatments",level:"1"},{id:"sec_9_2",title:"6.1 Pharmacological treatment",level:"2"},{id:"sec_9_3",title:"6.1.1 Physical therapy",level:"3"},{id:"sec_9_4",title:"6.1.1.1 Ultrasound",level:"4"},{id:"sec_10_4",title:"6.1.1.2 Transcutaneous electrical nerve stimulation (TENS)",level:"4"},{id:"sec_11_4",title:"6.1.1.3 Electromagnetic therapy",level:"4"},{id:"sec_12_4",title:"6.1.1.4 Extracorporeal shock wave therapy (ESWT)",level:"4"},{id:"sec_15_2",title:"6.2 Physiotherapy",level:"2"},{id:"sec_16_2",title:"6.3 Manual myofascial therapy",level:"2"},{id:"sec_17_2",title:"6.4 Minimally invasive treatments",level:"2"},{id:"sec_17_3",title:"6.4.1 Acupuncture",level:"3"},{id:"sec_17_4",title:"6.4.1.1 Kinesio taping (KT)",level:"4"},{id:"sec_20_2",title:"6.5 Operative treatments",level:"2"},{id:"sec_20_3",title:"6.5.1 Arthroscopic capsular release and manipulation under anesthetic (MUA)",level:"3"},{id:"sec_22_2",title:"6.6 Postural educational program",level:"2"},{id:"sec_22_3",title:"Table 3.",level:"3"},{id:"sec_25",title:"7. 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The Clinical Journal of Pain. 2001;17(1):33-46'},{id:"B45",body:'Robertson VJ, Baker KG. A review of therapeutic ultrasound - effectiveness studies. Physical Therapy. 2001;81(7):1339-1350'},{id:"B46",body:'Basford JR. Therapeutic physical agents. In: De Lisa JA, editor. Physical Medicine and Rehabilitation. Principles and Practice. Philadelphia: Lippincott Williams & Wilkins; 2005. p. 251e70'},{id:"B47",body:'Saggini R, Di Stefano A, Carmignano SM, Ancona E, Di Felice PA, Bellomo RG. The Treatment with Low Frequency Acoustic Pressure Waves (Ultrasound Diathermy): Therapeutic Considerations and Clinical Reports. University Centre of Physical and Rehabilitation Medicine, Chair of Physical and Rehabilitation Medicine, University "G. d\'Annunzio" – Chieti. Available from: https://www.researchgate.net/publication/322065721_'},{id:"B48",body:'Bellomo RG, Carmignano S, Saggini R. The applied mechanical vibration as ultrasound energy. In: Saggini R, editor. 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The effect of anterior versus posterior glide joint mobilization on external rotation range of motion in patients with shoulder adhesive capsulitis. Journal of Orthopaedic & Sports Physical Therap. 2007;37:88-99. DOI: 10.2519/jospt.2007.2307'},{id:"B61",body:'Nicholson GG et al. The Journal of Orthopaedic and Sports Physical Therapy. 1985;6:238-246. BMJ. 1998;317:1292-1296'},{id:"B62",body:'Vermeulen HM, Rozing PM, Obermann WR, le Cessie S, Vliet Vlieland TP. Comparison of high-grade and low-grade mobilization techniques in the management of adhesive capsulitis of the shoulder: Randomizedcontrolled trial. Physical Therapy. 2006;86:355-368'},{id:"B63",body:'Hing W, Hall T, Rivett D, Vicenzino B, Mulligan B. The Mulligan Concept of Manual Therapy-eBook: Textbook of Techniques. Elsevier Health Sciences; 2014'},{id:"B64",body:'Stathopoulos N, Dimitriadis Z, Koumantakis GA. Effectiveness of Mulligan\'s mobilization with movement techniques on pain and disability of peripheral joints: A systematic review with meta-analysis between 2008-2017. Physiotherapy. 2019;105(1):1-9. DOI: 10.1016/j.physio.2018.10.001. Epub 2018. Erratum in: Physiotherapy. 2020;106:219'},{id:"B65",body:'Elson L, Gardiner J. For more exercises to improve your balance and prevent falls, increase your flexibility, and even help relieve arthritis, back, and knee pain, buy the Harvard Special Health Report Stretching: 35 exercises to improve flexibility and reduce pain'},{id:"B66",body:'Yang JL, Chang CW, Chen SY, Wang SF, Lin JJ. Mobilization techniques in subjects with frozen shoulder syndrome: Randomized multiple-treatment trial. Physical Therapy. 2007;87:1307-1315'},{id:"B67",body:'Tukmachi ES. Frozen shoulder: A comparison of Western and traditional Chinese approaches and a clinical study of its acupuncture treatment. Acupuncture in Medicine. 1999;17(1):9-21. DOI: 10.1136/aim.17.1.9'},{id:"B68",body:'Liu Z editor. Essentials of Chinese Acupuncture. Beijing: Academy of Traditional Chinese Medicine. Springer-Verlag London; 2009. doi: 10.1007/978-1-84882-593-2'},{id:"B69",body:'Panchbudhe SA, Praveen Kumar Y, Choudhary S, Chiwhane A. Role of the physiotherapist to promote physical activity in physical therapy settings: A questionnaire study. International Journal of Current Research and Review. 2020;12(14 Special Issue):50-55'},{id:"B70",body:'Deshmukh N, Kahile M, Chaudhary N, Panchbhudhe S, Gawande V. Adjunct effect of kinesiotaping on adhesive capsulities of shoulder joint. International Journal of Current Research and Review. 2021;13(08):48-53. DOI: 10.31782/IJCRR.2021.13818'},{id:"B71",body:'Hanchard NC, Goodchild L, Thompson J, O\'Brien T, Davison D, Richardson C. Evidence-based clinical guidelines for the diagnosis, assessment and physiotherapy management of contracted (frozen) shoulder: Quick reference summary. Physiotherapy. 2012;98(2):117-120. DOI: 10.1016/j.physio.2012.01.001'},{id:"B72",body:'Stupay K, Neviaser A. Management of adhesive capsulitis. Orthopedic Research and Reviews. 2015;7:83-94. DOI: 10.2147/ORR.S56317'},{id:"B73",body:'Saggini R, Bellomo RG, Carmignano SM, Saggini A. Cancer pain—The role of an integrated, comprehensive rehabilitation program in its management. In: Leticia R, Silva IV, editors. Updates on Cancer Treatment. Rijeka: Edit IntechOpen; 2015. DOI: 10.5772/60548'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Simona Maria Carmignano",address:"simona.carmignano@gmail.com",affiliation:'
Department of Medicine, Surgery and Dentistry, “Salernitan Medical School”, University of Salerno, Italy
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Our platform – IntechOpen is the world’s leading publisher of OA books, built by scientists, for scientists.
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Our reputation – Everything we publish goes through a two-stage peer review process. We’re proud to count Nobel laureates among our esteemed authors. We meet European Commission standards for funding, and the research we’ve published has been funded by the Bill and Melinda Gates Foundation and the Wellcome Trust, among others. IntechOpen is a member of all relevant trade associations (including the STM Association and the Association of Learned and Professional Society Publishers) and has a selection of books indexed in Web of Science's Book Citation Index.
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A dedicated Author Service Manager to guide the publication process from submission to publication;
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Professional cover design;
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Unfortunately, the comorbidities of aging have created a large economic and health burden on society. Osteoporosis is the most prevalent age-related disease. It is characterized by uncoupled bone resorption that leads to low bone mass, compromised microarchitecture and structural deterioration that increases the likelihood of fracture with minimal trauma, known as fragility fractures. These fractures lead to disproportionally high mortality rate and a drastic decline in quality of life for those affected. While estrogen loss is one known trigger of osteoporosis, a number of recent studies have shown that osteoporosis is a multifactorial condition in both humans and rodent models. The presence or absence of certain factors are likely to determine which subset of the population develop osteoporosis. 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Therefore, the objective of this chapter is to describe the use of these parameters as an auxiliary mechanism in the detection of low bone mineral density, as well as to characterize the radiographic findings of patients with osteoporosis.",book:{id:"10323",slug:"osteoporosis-recent-advances-new-perspectives-and-applications",title:"Osteoporosis",fullTitle:"Osteoporosis - Recent Advances, New Perspectives and Applications"},signatures:"Plauto Christopher Aranha Watanabe, Giovani Antonio Rodrigues, Marcelo Rodrigues Azenha, Michel Campos Ribeiro, Enéas de Almeida Souza Filho, Rafael Angelo Soares Vieira and Fabio Santos Bottacin",authors:[{id:"76171",title:"Prof.",name:"Plauto C. A.",middleName:null,surname:"Watanabe",slug:"plauto-c.-a.-watanabe",fullName:"Plauto C. A. 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Anti-inflammatory diet is designed to improve health and prevent the occurrence and development of chronic diseases associated with inadequate diet. Proper nutrition is based on the anti-inflammatory pyramid and changes in poor eating habits are the long-term strategy for preventing inflammation and chronic diseases. Inflammatory factors from food may play a role in the development of osteoporosis and an anti-inflammatory diet may be a way to control and reduce long-term inflammation and prevent bone loss. Pro-inflammatory cytokines from the fat tissue, through activation of the RANKL/RANK/OPG system could intervene with bone metabolism in a way of increased bone loss. Therefore the special attention need to be given to obese patients due to twofold risk, one related to pro-inflammatory cytokines release and the other related to the deprivation of the vitamin D in the fat tissue.",book:{id:"10323",slug:"osteoporosis-recent-advances-new-perspectives-and-applications",title:"Osteoporosis",fullTitle:"Osteoporosis - Recent Advances, New Perspectives and Applications"},signatures:"Olga Cvijanović Peloza, Sandra Pavičić Žeželj, Gordana Kenđel Jovanović, Ivana Pavičić, Ana Terezija Jerbić Radetić, Sanja Zoričić Cvek, Jasna Lulić Drenjak, Gordana Starčević Klasan, Ariana Fužinac Smojver and Juraj Arbanas",authors:[{id:"339281",title:"Associate Prof.",name:"Olga",middleName:null,surname:"Cvijanović Peloza",slug:"olga-cvijanovic-peloza",fullName:"Olga Cvijanović Peloza"},{id:"346420",title:"Prof.",name:"Sandra",middleName:null,surname:"Pavičić Žeželj",slug:"sandra-pavicic-zezelj",fullName:"Sandra Pavičić Žeželj"},{id:"346421",title:"BSc.",name:"Ivana",middleName:null,surname:"Pavičić",slug:"ivana-pavicic",fullName:"Ivana Pavičić"},{id:"346423",title:"Prof.",name:"Ana Terezija",middleName:null,surname:"Jerbić Radetić",slug:"ana-terezija-jerbic-radetic",fullName:"Ana Terezija Jerbić Radetić"},{id:"346424",title:"Prof.",name:"Sanja",middleName:null,surname:"Zoričić Cvek",slug:"sanja-zoricic-cvek",fullName:"Sanja Zoričić Cvek"},{id:"346426",title:"MSc.",name:"Jasna",middleName:null,surname:"Lulić Drenjak",slug:"jasna-lulic-drenjak",fullName:"Jasna Lulić Drenjak"},{id:"346427",title:"Prof.",name:"Gordana",middleName:null,surname:"Starčević Klasan",slug:"gordana-starcevic-klasan",fullName:"Gordana Starčević Klasan"},{id:"346428",title:"MSc.",name:"Ariana",middleName:null,surname:"Fužinac Smojver",slug:"ariana-fuzinac-smojver",fullName:"Ariana Fužinac Smojver"},{id:"346429",title:"Prof.",name:"Juraj",middleName:null,surname:"Arbanas",slug:"juraj-arbanas",fullName:"Juraj Arbanas"},{id:"350011",title:"Dr.",name:"Gordana",middleName:null,surname:"Kenđel Jovanović",slug:"gordana-kendjel-jovanovic",fullName:"Gordana Kenđel Jovanović"}]},{id:"76351",doi:"10.5772/intechopen.97416",title:"Glucocorticoid-Induced Osteoporosis",slug:"glucocorticoid-induced-osteoporosis",totalDownloads:246,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The use of glucocorticoids (GC) in the medium and long term, causes several considerable side effects, being one of the main ones the reduction of bone mineral density (BMD). Prolonged corticosteroid therapy reduces BMD by up to 20% in trabecular bone and approximately 2–3% in cortical bone in the first year of use. This loss rate declines and stabilizes at approximately 2% in subsequent years. Therefore, there is a considerable increase in the incidence of pathological fractures, whether clinically symptomatic or asymptomatic (detected as a radiological finding), which varies between 30 and 50% of patients who use GC for more than three months. In view of the above, it is essential to prevent fractures and treat osteoporosis in patients using glucocorticoids for long periods (in particular, greater than or equal to 3 months), which may or may not be associated with clinical risk factors or previous fractures. The guidelines for the treatment and prevention of this comorbidity are well established for postmenopausal women and men over 50 years of age. 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Unfortunately, the comorbidities of aging have created a large economic and health burden on society. Osteoporosis is the most prevalent age-related disease. It is characterized by uncoupled bone resorption that leads to low bone mass, compromised microarchitecture and structural deterioration that increases the likelihood of fracture with minimal trauma, known as fragility fractures. These fractures lead to disproportionally high mortality rate and a drastic decline in quality of life for those affected. While estrogen loss is one known trigger of osteoporosis, a number of recent studies have shown that osteoporosis is a multifactorial condition in both humans and rodent models. The presence or absence of certain factors are likely to determine which subset of the population develop osteoporosis. In this chapter, we review the factors that contribute to osteoporosis with an emphasis on its multifactorial nature and the therapeutic consequences.",book:{id:"10323",slug:"osteoporosis-recent-advances-new-perspectives-and-applications",title:"Osteoporosis",fullTitle:"Osteoporosis - Recent Advances, New Perspectives and Applications"},signatures:"Di Wu, Anna Cline-Smith, Elena Shashkova and Rajeev Aurora",authors:[{id:"339667",title:"Associate Prof.",name:"Rajeev",middleName:null,surname:"Aurora",slug:"rajeev-aurora",fullName:"Rajeev Aurora"},{id:"347366",title:"Mr.",name:"Di",middleName:null,surname:"Wu",slug:"di-wu",fullName:"Di Wu"},{id:"347367",title:"Ms.",name:"Anna",middleName:null,surname:"Cline-Smith",slug:"anna-cline-smith",fullName:"Anna Cline-Smith"},{id:"347579",title:"Dr.",name:"Elena",middleName:null,surname:"Shashkova",slug:"elena-shashkova",fullName:"Elena Shashkova"}]},{id:"75742",title:"Osteoporosis and Dietary Inflammatory Index",slug:"osteoporosis-and-dietary-inflammatory-index",totalDownloads:230,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Healthy bones are constantly being renewed and proper nutrition is an important factor in this process. Anti-inflammatory diet is designed to improve health and prevent the occurrence and development of chronic diseases associated with inadequate diet. Proper nutrition is based on the anti-inflammatory pyramid and changes in poor eating habits are the long-term strategy for preventing inflammation and chronic diseases. Inflammatory factors from food may play a role in the development of osteoporosis and an anti-inflammatory diet may be a way to control and reduce long-term inflammation and prevent bone loss. Pro-inflammatory cytokines from the fat tissue, through activation of the RANKL/RANK/OPG system could intervene with bone metabolism in a way of increased bone loss. Therefore the special attention need to be given to obese patients due to twofold risk, one related to pro-inflammatory cytokines release and the other related to the deprivation of the vitamin D in the fat tissue.",book:{id:"10323",slug:"osteoporosis-recent-advances-new-perspectives-and-applications",title:"Osteoporosis",fullTitle:"Osteoporosis - Recent Advances, New Perspectives and Applications"},signatures:"Olga Cvijanović Peloza, Sandra Pavičić Žeželj, Gordana Kenđel Jovanović, Ivana Pavičić, Ana Terezija Jerbić Radetić, Sanja Zoričić Cvek, Jasna Lulić Drenjak, Gordana Starčević Klasan, Ariana Fužinac Smojver and Juraj Arbanas",authors:[{id:"339281",title:"Associate Prof.",name:"Olga",middleName:null,surname:"Cvijanović Peloza",slug:"olga-cvijanovic-peloza",fullName:"Olga Cvijanović Peloza"},{id:"346420",title:"Prof.",name:"Sandra",middleName:null,surname:"Pavičić Žeželj",slug:"sandra-pavicic-zezelj",fullName:"Sandra Pavičić Žeželj"},{id:"346421",title:"BSc.",name:"Ivana",middleName:null,surname:"Pavičić",slug:"ivana-pavicic",fullName:"Ivana Pavičić"},{id:"346423",title:"Prof.",name:"Ana Terezija",middleName:null,surname:"Jerbić Radetić",slug:"ana-terezija-jerbic-radetic",fullName:"Ana Terezija Jerbić Radetić"},{id:"346424",title:"Prof.",name:"Sanja",middleName:null,surname:"Zoričić Cvek",slug:"sanja-zoricic-cvek",fullName:"Sanja Zoričić Cvek"},{id:"346426",title:"MSc.",name:"Jasna",middleName:null,surname:"Lulić Drenjak",slug:"jasna-lulic-drenjak",fullName:"Jasna Lulić Drenjak"},{id:"346427",title:"Prof.",name:"Gordana",middleName:null,surname:"Starčević Klasan",slug:"gordana-starcevic-klasan",fullName:"Gordana Starčević Klasan"},{id:"346428",title:"MSc.",name:"Ariana",middleName:null,surname:"Fužinac Smojver",slug:"ariana-fuzinac-smojver",fullName:"Ariana Fužinac Smojver"},{id:"346429",title:"Prof.",name:"Juraj",middleName:null,surname:"Arbanas",slug:"juraj-arbanas",fullName:"Juraj Arbanas"},{id:"350011",title:"Dr.",name:"Gordana",middleName:null,surname:"Kenđel Jovanović",slug:"gordana-kendjel-jovanovic",fullName:"Gordana Kenđel Jovanović"}]},{id:"76351",title:"Glucocorticoid-Induced Osteoporosis",slug:"glucocorticoid-induced-osteoporosis",totalDownloads:248,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The use of glucocorticoids (GC) in the medium and long term, causes several considerable side effects, being one of the main ones the reduction of bone mineral density (BMD). Prolonged corticosteroid therapy reduces BMD by up to 20% in trabecular bone and approximately 2–3% in cortical bone in the first year of use. This loss rate declines and stabilizes at approximately 2% in subsequent years. Therefore, there is a considerable increase in the incidence of pathological fractures, whether clinically symptomatic or asymptomatic (detected as a radiological finding), which varies between 30 and 50% of patients who use GC for more than three months. In view of the above, it is essential to prevent fractures and treat osteoporosis in patients using glucocorticoids for long periods (in particular, greater than or equal to 3 months), which may or may not be associated with clinical risk factors or previous fractures. The guidelines for the treatment and prevention of this comorbidity are well established for postmenopausal women and men over 50 years of age. However, for patients below this range, studies are still lacking.",book:{id:"10323",slug:"osteoporosis-recent-advances-new-perspectives-and-applications",title:"Osteoporosis",fullTitle:"Osteoporosis - Recent Advances, New Perspectives and Applications"},signatures:"José Renan Vieira da Costa Júnior and Sérgio Luchini Batista",authors:[{id:"164388",title:"Prof.",name:"Sergio",middleName:null,surname:"Luchini Batista",slug:"sergio-luchini-batista",fullName:"Sergio Luchini Batista"},{id:"354032",title:"Dr.",name:"José Renan",middleName:null,surname:"Vieira Da Costa Júnior",slug:"jose-renan-vieira-da-costa-junior",fullName:"José Renan Vieira Da Costa Júnior"}]}],onlineFirstChaptersFilter:{topicId:"1414",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},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:8,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:98,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:286,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:106,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:9,numberOfPublishedChapters:101,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:11,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"24",title:"Sustainable Development",doi:"10.5772/intechopen.100361",issn:null,scope:"
\r\n\tTransforming our World: the 2030 Agenda for Sustainable Development endorsed by United Nations and 193 Member States, came into effect on Jan 1, 2016, to guide decision making and actions to the year 2030 and beyond. Central to this Agenda are 17 Goals, 169 associated targets and over 230 indicators that are reviewed annually. The vision envisaged in the implementation of the SDGs is centered on the five Ps: People, Planet, Prosperity, Peace and Partnership. This call for renewed focused efforts ensure we have a safe and healthy planet for current and future generations.
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\r\n\t
\r\n
\r\n\tThis Series focuses on covering research and applied research involving the five Ps through the following topics:
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\r\n\t1. Sustainable Economy and Fair Society that relates to SDG 1 on No Poverty, SDG 2 on Zero Hunger, SDG 8 on Decent Work and Economic Growth, SDG 10 on Reduced Inequalities, SDG 12 on Responsible Consumption and Production, and SDG 17 Partnership for the Goals
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\r\n\t2. Health and Wellbeing focusing on SDG 3 on Good Health and Wellbeing and SDG 6 on Clean Water and Sanitation
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\r\n\t
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\r\n\t3. Inclusivity and Social Equality involving SDG 4 on Quality Education, SDG 5 on Gender Equality, and SDG 16 on Peace, Justice and Strong Institutions
\r\n
\r\n\t
\r\n
\r\n\t4. Climate Change and Environmental Sustainability comprising SDG 13 on Climate Action, SDG 14 on Life Below Water, and SDG 15 on Life on Land
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\r\n\t
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\r\n\t5. Urban Planning and Environmental Management embracing SDG 7 on Affordable Clean Energy, SDG 9 on Industry, Innovation and Infrastructure, and SDG 11 on Sustainable Cities and Communities.
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\r\n\tThe series also seeks to support the use of cross cutting SDGs, as many of the goals listed above, targets and indicators are all interconnected to impact our lives and the decisions we make on a daily basis, making them impossible to tie to a single topic.
",coverUrl:"https://cdn.intechopen.com/series/covers/24.jpg",latestPublicationDate:"April 24th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:0,editor:{id:"262440",title:"Prof.",name:"Usha",middleName:null,surname:"Iyer-Raniga",slug:"usha-iyer-raniga",fullName:"Usha Iyer-Raniga",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRYSXQA4/Profile_Picture_2022-02-28T13:55:36.jpeg",biography:"Usha Iyer-Raniga is a professor in the School of Property and Construction Management at RMIT University. Usha co-leads the One Planet Network’s Sustainable Buildings and Construction Programme (SBC), a United Nations 10 Year Framework of Programmes on Sustainable Consumption and Production (UN 10FYP SCP) aligned with Sustainable Development Goal 12. The work also directly impacts SDG 11 on Sustainable Cities and Communities. She completed her undergraduate degree as an architect before obtaining her Masters degree from Canada and her Doctorate in Australia. Usha has been a keynote speaker as well as an invited speaker at national and international conferences, seminars and workshops. Her teaching experience includes teaching in Asian countries. She has advised Austrade, APEC, national, state and local governments. She serves as a reviewer and a member of the scientific committee for national and international refereed journals and refereed conferences. She is on the editorial board for refereed journals and has worked on Special Issues. Usha has served and continues to serve on the Boards of several not-for-profit organisations and she has also served as panel judge for a number of awards including the Premiers Sustainability Award in Victoria and the International Green Gown Awards. Usha has published over 100 publications, including research and consulting reports. Her publications cover a wide range of scientific and technical research publications that include edited books, book chapters, refereed journals, refereed conference papers and reports for local, state and federal government clients. She has also produced podcasts for various organisations and participated in media interviews. She has received state, national and international funding worth over USD $25 million. Usha has been awarded the Quarterly Franklin Membership by London Journals Press (UK). Her biography has been included in the Marquis Who's Who in the World® 2018, 2016 (33rd Edition), along with approximately 55,000 of the most accomplished men and women from around the world, including luminaries as U.N. Secretary-General Ban Ki-moon. In 2017, Usha was awarded the Marquis Who’s Who Lifetime Achiever Award.",institutionString:null,institution:{name:"RMIT University",institutionURL:null,country:{name:"Australia"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:5,paginationItems:[{id:"91",title:"Sustainable Economy and Fair Society",coverUrl:"https://cdn.intechopen.com/series_topics/covers/91.jpg",isOpenForSubmission:!0,annualVolume:11975,editor:{id:"181603",title:"Dr.",name:"Antonella",middleName:null,surname:"Petrillo",slug:"antonella-petrillo",fullName:"Antonella Petrillo",profilePictureURL:"https://mts.intechopen.com/storage/users/181603/images/system/181603.jpg",biography:"Antonella Petrillo is a Professor at the Department of Engineering of the University of Naples “Parthenope”, Italy. She received her Ph.D. in Mechanical Engineering from the University of Cassino. Her research interests include multi-criteria decision analysis, industrial plant, logistics, manufacturing and safety. She serves as an Associate Editor for the International Journal of the Analytic Hierarchy Process. She is a member of AHP Academy and a member of several editorial boards. 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Her focus is on quality, innovation, leadership, and personalised learning. She works primarily at the strategic and policy levels, both nationally and internationally, and with key international organisations. She is committed to promoting and improving OFDL in the context of SDG4 and the future of education. Ossiannilsson has more than 20 years of experience in her current field, but more than 40 years in the education sector. She works as a reviewer and expert for the European Commission and collaborates with the Joint Research Centre for Quality in Open Education. Ossiannilsson also collaborates with ITCILO and ICoBC (International Council on Badges and Credentials). She is a member of the ICDE Board of Directors and has previously served on the boards of EDEN and EUCEN. Ossiannilsson is a quality expert and reviewer for ICDE, EDEN and the EADTU. She chairs the ICDE OER Advocacy Committee and is a member of the ICDE Quality Network. She is regularly invited as a keynote speaker at conferences. She is a guest editor for several special issues and a member of the editorial board of several scientific journals. She has published more than 200 articles and is currently working on book projects in the field of OFDL. Ossiannilsson is a visiting professor at several international universities and was recently appointed Professor and Research Fellow at Victoria University of Wellington, NZ. Ossiannilsson has been awarded the following fellowships: EDEN Fellows, EDEN Council of Fellows, and Open Education Europe. She is a ICDE OER Ambassador, Open Education Europe Ambassador, GIZ Ambassador for Quality in Digital Learning, and part of the Globe-Community of Digital Learning and Champion of SPARC Europe. On a national level, she is a quality developer at the Swedish Institute for Standards (SIS) and for ISO. She is a member of the Digital Skills and Jobs Coalition Sweden and Vice President of the Swedish Association for Distance Education. She is currently working on a government initiative on quality in distance education at the National Council for Higher Education. She holds a Ph.D. from the University of Oulu, Finland.',institutionString:"Swedish Association for Distance Education, Sweden",institution:null},editorTwo:null,editorThree:null},{id:"94",title:"Climate Change and Environmental Sustainability",coverUrl:"https://cdn.intechopen.com/series_topics/covers/94.jpg",isOpenForSubmission:!1,annualVolume:null,editor:null,editorTwo:null,editorThree:null},{id:"95",title:"Urban Planning and Environmental Management",coverUrl:"https://cdn.intechopen.com/series_topics/covers/95.jpg",isOpenForSubmission:!0,annualVolume:11979,editor:{id:"181079",title:"Dr.",name:"Christoph",middleName:null,surname:"Lüthi",slug:"christoph-luthi",fullName:"Christoph Lüthi",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRHSqQAO/Profile_Picture_2022-04-12T15:51:33.png",biography:"Dr. Christoph Lüthi is an urban infrastructure planner with over 25 years of experience in planning and design of urban infrastructure in middle and low-income countries. He holds a Master’s Degree in Urban Development Planning from the University College of London (UCL), and a Ph.D. in Urban Planning & Engineering from TU Berlin. He has conducted applied research on urban planning and infrastructure issues in over 20 countries in Africa and Asia. In 2005 he joined Eawag-Sandec as Leader of the Strategic Environmental Sanitation Planning Group. Since 2015 he heads the research department Sanitation, Water and Solid Waste for Development (Sandec) at the Swiss Federal Institute of Aquatic Research and Technology (Eawag).",institutionString:"Swiss Federal Institute of Aquatic Science and Technology, Switzerland",institution:null},editorTwo:{id:"290571",title:"Dr.",name:"Rui Alexandre",middleName:null,surname:"Castanho",slug:"rui-alexandre-castanho",fullName:"Rui Alexandre Castanho",profilePictureURL:"https://mts.intechopen.com/storage/users/290571/images/system/290571.jpg",biography:"Rui Alexandre Castanho has a master\\'s degree in Planning, Audit, and Control in Urban Green Spaces and an international Ph.D. in Sustainable Planning in Borderlands. Currently, he is a professor at WSB University, Poland, and a visiting professor at the University of Johannesburg, South Africa. Dr. Castanho is a post-doc researcher on the GREAT Project, University of Azores, Ponta Delgada, Portugal. He collaborates with the Environmental Resources Analysis Research Group (ARAM), University of Extremadura (UEx), Spain; VALORIZA - Research Center for the Enhancement of Endogenous Resources, Polytechnic Institute of Portalegre (IPP), Portugal; Centre for Tourism Research, Development and Innovation (CITUR), Madeira, Portugal; and AQUAGEO Research Group, University of Campinas (UNICAMP), Brazil.",institutionString:"University of Johannesburg, South Africa and WSB University, Poland",institution:{name:"University of Johannesburg",institutionURL:null,country:{name:"South Africa"}}},editorThree:null}]},overviewPageOFChapters:{paginationCount:26,paginationItems:[{id:"81791",title:"Self-Supervised Contrastive Representation Learning in Computer Vision",doi:"10.5772/intechopen.104785",signatures:"Yalin Bastanlar and Semih Orhan",slug:"self-supervised-contrastive-representation-learning-in-computer-vision",totalDownloads:3,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Pattern Recognition - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11442.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"79345",title:"Application of Jump Diffusion Models in Insurance Claim Estimation",doi:"10.5772/intechopen.99853",signatures:"Leonard Mushunje, Chiedza Elvina Mashiri, Edina Chandiwana and Maxwell Mashasha",slug:"application-of-jump-diffusion-models-in-insurance-claim-estimation-1",totalDownloads:2,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Data Clustering",coverURL:"https://cdn.intechopen.com/books/images_new/10820.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"81557",title:"Object Tracking Using Adapted Optical Flow",doi:"10.5772/intechopen.102863",signatures:"Ronaldo Ferreira, Joaquim José de Castro Ferreira and António José Ribeiro Neves",slug:"object-tracking-using-adapted-optical-flow",totalDownloads:10,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Information Extraction and Object Tracking in Digital Video",coverURL:"https://cdn.intechopen.com/books/images_new/10652.jpg",subseries:{id:"24",title:"Computer Vision"}}},{id:"81558",title:"Thresholding Image Techniques for Plant Segmentation",doi:"10.5772/intechopen.104587",signatures:"Miguel Ángel Castillo-Martínez, Francisco Javier Gallegos-Funes, Blanca E. 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He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,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. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. 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:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. 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\r\n\tIn general, the harsher the environmental conditions in an ecosystem, the lower the biodiversity. Changes in the environment caused by human activity accelerate the impoverishment of biodiversity.
\r\n
\r\n\tBiodiversity refers to “the variability of living organisms from any source, including terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part; it includes diversity within each species, between species, and that of ecosystems”.
\r\n
\r\n\tBiodiversity provides food security and constitutes a gene pool for biotechnology, especially in the field of agriculture and medicine, and promotes the development of ecotourism.
\r\n
\r\n\tCurrently, biologists admit that we are witnessing the first phases of the seventh mass extinction caused by human intervention. It is estimated that the current rate of extinction is between a hundred and a thousand times faster than it was when man first appeared. The disappearance of species is caused not only by an accelerated rate of extinction, but also by a decrease in the rate of emergence of new species as human activities degrade the natural environment. The conservation of biological diversity is "a common concern of humanity" and an integral part of the development process. Its objectives are “the conservation of biological diversity, the sustainable use of its components, and the fair and equitable sharing of the benefits resulting from the use of genetic resources”.
\r\n
\r\n\tThe following are the main causes of biodiversity loss:
\r\n
\r\n\t• The destruction of natural habitats to expand urban and agricultural areas and to obtain timber, minerals and other natural resources.
\r\n
\r\n\t• The introduction of alien species into a habitat, whether intentionally or unintentionally which has an impact on the fauna and flora of the area, and as a result, they are reduced or become extinct.
\r\n
\r\n\t• Pollution from industrial and agricultural products, which devastate the fauna and flora, especially those in fresh water.
\r\n
\r\n\t• Global warming, which is seen as a threat to biological diversity, and will become increasingly important in the future.
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The combination of electronics and computer science with biology and medicine has improved patient diagnosis, reduced rehabilitation time, and helped to facilitate a better quality of life. Nowadays, all medical imaging devices, medical instruments, or new laboratory techniques result from the cooperation of specialists in various fields. The series of Biomedical Engineering books covers such areas of knowledge as chemistry, physics, electronics, medicine, and biology. 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Dr. Koprowski has authored more than a hundred research papers with dozens in impact factor (IF) journals and has authored or co-authored six books. Additionally, he is the author of several national and international patents in the field of biomedical devices and imaging. Since 2011, he has been a reviewer of grants and projects (including EU projects) in biomedical engineering.",institutionString:null,institution:{name:"University of Silesia",institutionURL:null,country:{name:"Poland"}}},subseries:[{id:"7",title:"Bioinformatics and Medical Informatics",keywords:"Biomedical Data, Drug Discovery, Clinical Diagnostics, Decoding Human Genome, AI in Personalized Medicine, Disease-prevention Strategies, Big Data Analysis in Medicine",scope:"Bioinformatics aims to help understand the functioning of the mechanisms of living organisms through the construction and use of quantitative tools. The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",annualVolume:11403,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"5886",title:"Dr.",name:"Alexandros",middleName:"T.",surname:"Tzallas",fullName:"Alexandros Tzallas",profilePictureURL:"https://mts.intechopen.com/storage/users/5886/images/system/5886.png",institutionString:"University of Ioannina, Greece & Imperial College London",institution:{name:"University of Ioannina",institutionURL:null,country:{name:"Greece"}}},{id:"257388",title:"Distinguished Prof.",name:"Lulu",middleName:null,surname:"Wang",fullName:"Lulu Wang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRX6kQAG/Profile_Picture_1630329584194",institutionString:null,institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda",middleName:"R.",surname:"Gharieb",fullName:"Reda Gharieb",profilePictureURL:"https://mts.intechopen.com/storage/users/225387/images/system/225387.jpg",institutionString:"Assiut University",institution:{name:"Assiut University",institutionURL:null,country:{name:"Egypt"}}}]},{id:"8",title:"Bioinspired Technology and Biomechanics",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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