\\n\\n
IntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\\n\\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\\n\\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\\n\\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\\n\\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\\n\\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\\n\\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\\n\\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\\n\\nFeel free to share this news on social media and help us mark this memorable moment!
\\n\\n\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/237"}},components:[{type:"htmlEditorComponent",content:'
After years of being acknowledged as the world's leading publisher of Open Access books, today, we are proud to announce we’ve successfully launched a portfolio of Open Science journals covering rapidly expanding areas of interdisciplinary research.
\n\n\n\nIntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\n\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\n\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\n\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\n\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\n\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\n\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\n\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\n\nFeel free to share this news on social media and help us mark this memorable moment!
\n\n\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"7795",leadTitle:null,fullTitle:"Artificial Intelligence - Scope and Limitations",title:"Artificial Intelligence",subtitle:"Scope and Limitations",reviewType:"peer-reviewed",abstract:"Artificial intelligence (AI) is a potent buzzword and happening technology which has greatly impacted the lifestyle of every human being either directly or indirectly and is shaping the future of tomorrow. In fact, AI is fast becoming an intrinsic part of our daily life and is not confined to university research labs, even if remarkable progress has been made in this domain. The benefit of this phenomenon is widely recognized in diversified areas, ranging from medicine to security to consumer applications and business, and resulting in improvements in the quality of life of humankind. Every new disruptive technology has its own pros and cons and AI is no exception to this rule. Privacy, data protection, and the rights of individuals pose social and ethical challenges.",isbn:"978-1-78985-936-2",printIsbn:"978-1-78985-935-5",pdfIsbn:"978-1-83962-124-6",doi:"10.5772/intechopen.77611",price:100,priceEur:109,priceUsd:129,slug:"artificial-intelligence-scope-and-limitations",numberOfPages:78,isOpenForSubmission:!1,isInWos:1,isInBkci:!1,hash:"7e536b4fe8982ca9015228fe6f58c6ea",bookSignature:"Dinesh G. Harkut",publishedDate:"April 24th 2019",coverURL:"https://cdn.intechopen.com/books/images_new/7795.jpg",numberOfDownloads:5605,numberOfWosCitations:4,numberOfCrossrefCitations:4,numberOfCrossrefCitationsByBook:1,numberOfDimensionsCitations:9,numberOfDimensionsCitationsByBook:1,hasAltmetrics:1,numberOfTotalCitations:17,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"June 13th 2018",dateEndSecondStepPublish:"July 4th 2018",dateEndThirdStepPublish:"September 2nd 2018",dateEndFourthStepPublish:"November 21st 2018",dateEndFifthStepPublish:"January 20th 2019",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"216122",title:"Dr.",name:"Dinesh G.",middleName:null,surname:"Harkut",slug:"dinesh-g.-harkut",fullName:"Dinesh G. Harkut",profilePictureURL:"https://mts.intechopen.com/storage/users/216122/images/system/216122.png",biography:"Dr. Dinesh G. Harkut is Associate Professor at Prof Ram Meghe College of Engineering & Management (PRMCEAM), Badnera, India, in the Computer Science and Engineering Department. He obtained a bachelor’s degree, a master’s of engineering (CSE), and a PhD (CSE) from SGBAU Amravati University, Maharashtra, India. He also holds a master’s degree and PhD in Business Administration.\nHis primary research interests are in artificial intelligence, big data, analytics, embedded systems, and e-commerce. He has supervised eighteen master’s degree and twenty-four bachelor’s degree students. He has published forty-seven papers in refereed journals and published six books with international publishers. He has also organized various workshops, sessions, conferences, and trainings. He has two patents filed and published in his name in India. \nHe is a member of the Board of Studies (Computer Science and Engineering) and a recognized PhD supervisor at SGBAU Amravati University, Maharashtra, India. He holds membership in various professional bodies including the Institution of Electronics and Telecommunication Engineers (IETE), New Delhi; International Society for Technology in Education (ISTE), New Delhi; Universal Association of Computer and Electronics Engineers (UACEE), USA; International Economics Development and Research Center (IEDRC), Hong Kong; International Association of Engineers (IAENG), Hong Kong; and the European Alliance for Innovation, Belgium.",institutionString:"Prof Ram Mehge College of Engineering and Management",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"4",institution:{name:"Sant Gadge Baba Amravati University",institutionURL:null,country:{name:"India"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"87",title:"Artificial Intelligence",slug:"computer-and-information-science-artificial-intelligence"}],chapters:[{id:"66147",title:"Introductory Chapter: Artificial Intelligence - Challenges and Applications",doi:"10.5772/intechopen.84624",slug:"introductory-chapter-artificial-intelligence-challenges-and-applications",totalDownloads:1586,totalCrossrefCites:3,totalDimensionsCites:5,hasAltmetrics:1,abstract:null,signatures:"Dinesh G. Harkut and Kashmira Kasat",downloadPdfUrl:"/chapter/pdf-download/66147",previewPdfUrl:"/chapter/pdf-preview/66147",authors:[null],corrections:null},{id:"64306",title:"Intention to Use WhatsApp",doi:"10.5772/intechopen.81999",slug:"intention-to-use-whatsapp",totalDownloads:1059,totalCrossrefCites:1,totalDimensionsCites:4,hasAltmetrics:1,abstract:"More than 1.8 billion people use WhatsApp nowadays, out of which 70% uses it daily. In this scenario, this study seeks modeling the variables that positively influence the intention to use WhatsApp. To this end, 579 surveys based on the unified theory of acceptance and use of technology are conducted. The descriptive results show that individuals use WhatsApp mainly motivated by leisure. In this sense, according to the structural equation model, the variable with the greatest influence on behavioral intention is hedonic motivation, followed by social influence, performance expectancy, and effort expectancy. These results indicate that most people use WhatsApp principally because they find it fun, enjoyable, and very entertaining, something more inherent to an entertainment application than to a messaging application. Nevertheless, a cluster analysis indicates the existence of two consumer segments: one showing a certain indifference and disagreement regarding the usefulness of WhatsApp for their activities and duties and the other manifesting that it uses WhatsApp not only for leisure but also for work, academic, and informative reasons. These differences in consumer drivers might have a great impact on WhatsApp and its competition marketing strategies.",signatures:"Cristobal Fernández-Robin, Diego Yáñez and Scott McCoy",downloadPdfUrl:"/chapter/pdf-download/64306",previewPdfUrl:"/chapter/pdf-preview/64306",authors:[null],corrections:null},{id:"65222",title:"Information and Communication Systems Including Artificial Intelligence and Big Data as Objects of International Legal Protection",doi:"10.5772/intechopen.83565",slug:"information-and-communication-systems-including-artificial-intelligence-and-big-data-as-objects-of-i",totalDownloads:975,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The objective of this study is identifying prospective for international legal protection of information and communication systems including artificial intelligence on the universal and regional levels, and analysis of legal instruments for protection of artificial intelligence and Big Data in the context of regulation of relations in the global information society. A complex of general scientific and philosophical methods, including the logical, comparative-legal, formal-legal, systemic-structural, and problematic-theoretical methods, as well as methods of analysis and synthesis, generalization and description were used in the research. It was found that the existing international agreements in the field of intellectual property protection take no account of the particular features of protection of complex objects. Complex objects comprise information and communication systems including artificial intelligence and Big Data. There is an objective necessity to establish a legal regime for complex objects on the universal level. The findings can be used in activities of international organizations in execution of their functions of unification and harmonization of the international information law.",signatures:"Valentina Petrovna Talimonchik",downloadPdfUrl:"/chapter/pdf-download/65222",previewPdfUrl:"/chapter/pdf-preview/65222",authors:[{id:"248931",title:"Dr.",name:"Valentina",surname:"Talimonchik",slug:"valentina-talimonchik",fullName:"Valentina Talimonchik"}],corrections:null},{id:"64350",title:"Prediction of Cancer Patient Outcomes Based on Artificial Intelligence",doi:"10.5772/intechopen.81872",slug:"prediction-of-cancer-patient-outcomes-based-on-artificial-intelligence",totalDownloads:1180,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Knowledge-based outcome predictions are common before radiotherapy. Because there are various treatment techniques, numerous factors must be considered in predicting cancer patient outcomes. As expectations surrounding personalized radiotherapy using complex data have increased, studies on outcome predictions using artificial intelligence have also increased. Representative artificial intelligence techniques used to predict the outcomes of cancer patients in the field of radiation oncology include collecting and processing big data, text mining of clinical literature, and machine learning for implementing prediction models. Here, methods of data preparation and model construction to predict rates of survival and toxicity using artificial intelligence are described.",signatures:"Suk Lee, Eunbin Ju, Suk Woo Choi, Hyungju Lee, Jang Bo Shim,\nKyung Hwan Chang, Kwang Hyeon Kim and Chul Yong Kim",downloadPdfUrl:"/chapter/pdf-download/64350",previewPdfUrl:"/chapter/pdf-preview/64350",authors:[null],corrections:null},{id:"64304",title:"Team Exploration of Environments Using Stochastic Local Search",doi:"10.5772/intechopen.81902",slug:"team-exploration-of-environments-using-stochastic-local-search",totalDownloads:805,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"We investigate the use of Stochastic Local Search (SLS) technique to explore environments where agents? knowledge and the time to explore such environments are limited. We extend a work that uses evolutionary algorithms to evolve teams in simulated environments. Our work proposes a formalization of the concept of state and neighborhood for SLS and provides evaluation of agents? teams using number of interesting cells. Further, we modify the environments to include goals that are randomly distributed among interesting cells. Agents in this case are then required to search for goals. Experiments using teams of different sizes show the effectiveness of our technique. Teams were able to complete exploration of more than 70% of the environments, while in the best cases, they were able to complete explorations of more than 80% of the environments within limited time steps. These results compare with those of the previous work. It is interesting to note that all teams of agents were able to find on average all the goals in the three environments when the size of the grid is 12. This is a 100% achievement by the agents? teams. However, performance can be seen to degrade as the environments? sizes become larger.",signatures:"Ramoni O. Lasisi and Robert DuPont",downloadPdfUrl:"/chapter/pdf-download/64304",previewPdfUrl:"/chapter/pdf-preview/64304",authors:[null],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"9966",title:"Dynamic Data Assimilation",subtitle:"Beating the Uncertainties",isOpenForSubmission:!1,hash:"e7fde2a36354a2f5a4282fdf9c743380",slug:"dynamic-data-assimilation-beating-the-uncertainties",bookSignature:"Dinesh G. Harkut",coverURL:"https://cdn.intechopen.com/books/images_new/9966.jpg",editedByType:"Edited by",editors:[{id:"216122",title:"Dr.",name:"Dinesh G.",surname:"Harkut",slug:"dinesh-g.-harkut",fullName:"Dinesh G. 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The quantification of inertial sensor systems have been proposed for the measurement and quantification of human movement characteristics since approximately the mid-20th century. However, sufficient miniaturization and reliability regarding that timeframe had not been achieved for associated biomedical applications [11, 12, 13, 14, 15]. Motivating research, development, testing, and evaluation for the evolution of inertial sensors derived from industries extrinsic relative to the biomedical field, such as the automotive industry for regulation of airbag deployment [11, 12, 13, 15]. Upon the achievement of a sufficient threshold for miniaturization and reliability these inertial sensors have been successfully applied to numerous human movement scenarios, such as the quantification of reflex, gait, and movement disorder. Furthermore, these inertial sensors were noted as functionally wearable with wireless in capability, which ushered the presence of wearable and wireless inertial sensor systems for quantifying human movement [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]. A historical and evolutionary perspective leading to the amalgamation of inertial sensors that are functionally wearable with wireless connectivity to Cloud computing resources in conjunction with machine learning classification as an advanced post-processing technique, which is known as Network Centric Therapy, for the biomedical domain is presented.
Prior to the advent of wearable and wireless inertial sensor systems, the diagnosis of a subject’s health status was essentially derived from the expert although subjective interpretation of a skilled clinician. The clinician is generally tasked with the responsibility to interpret the health of the patient and apply the observation to an ordinal scale criteria methodology. This ordinal scale process is ubiquitous to the clinical domain, and this approach is relevant to the scope of reflex response, gait, and movement disorder. However, the ordinal scale strategy encompasses contention regarding reliability, and there generally does not exist a means for translating between various available ordinal scales [1, 3, 11, 12, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30].
Further issues with the ordinal scale approach are evident with respect to the imperative need for patient-clinician interaction. From a logistical perspective a patient is required to travel to a clinical appointment, which in the case of a specialized expert may require relatively long-distance travel. Additionally, the clinician is only provided with a short duration of time to interpret the patient’s health status, which may be in dispute to the true health condition of the patient. The ordinal scale approach intuitively only provides limited insight of patient health, for which sensor signal data may provide a more revealing historical perspective.
The acquisition of quantified sensor signal data enables more pertinent clinical acuity regarding the health status with respect to reflex response, gait, and movement disorder [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]. With respect to the quantification of reflex response an assortment of electro-mechanical sensor systems have been proposed. These devices generally have consisted of the means for evoking the reflex through a provisional reflex hammer and quantifying the correlated reflex response [17, 18, 31, 32, 33, 34, 35, 36, 37, 38]. By temporally synchronizing the input quantification device eliciting the reflex and output quantification sensor of the reflex response a functional reflex latency can be derived [17, 18, 39, 40].
The quantification of the input that commences the reflex has been demonstrated through instrumented provisional reflex hammers and motorized devices. These devices enable measuring of the intensity of the eliciting impact and the time stamp regarding the start of the reflex respective of the neurological pathway. The reflex response, such as deriving from the patellar tendon, can be measured through electromyograms (EMGs), strain gauges, optical motion cameras, force sensors, and wired inertial sensors in addition to the associated time stamp. The temporal differential between the evoking reflex input time stamp and the reflex response time stamp can derive a functional latency of the reflex under consideration, such as the latency of the patellar tendon reflex [17, 18, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44].
Electro-mechanical systems have been applied for the quantified assessment of gait, which also pertains to movement disorder conditions. Representative electro-mechanical apparatus for quantifying gait consist of EMGs, optical motion cameras, force plates, foot switches, electrogoniometers, and metabolic analysis devices. These devices are generally reserved for clinical gait laboratories and imply supervision from expert clinical resources [11, 12, 45, 46, 47, 48].
The acquired sensor signal data can be post-processed and applied to sophisticated techniques, such as machine learning, for distinguishing between various states of health during gait. Two particular types sensor signal are the force plate and optical motion camera [49, 50, 51, 52, 53, 54]. The force plate provides kinetic signal data, and the optical motion camera provides kinematic signal data. The force plate and optical motion camera can be operated in tandem and synchronicity to derive clinically significant information about gait, such as ankle torque derived during stance [48].
These electro-mechanical systems enable quantification of human movement features, such as reflex response, gait, and movement disorders, through the acquired sensor signal data [31, 32, 33, 34, 35, 36, 37, 38, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54]. Although these electro-mechanical systems are clinically standard, they are generally constrained to a clinical laboratory. Furthermore, the majority of these devices both require specialized resources for their experimental operation, and they are predominantly not portable [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 47, 48].
By contrast, the functionally wearable with wireless inertial sensor system considerably alleviates the constraints of specialized resources through simplified means of activating the inertial sensor signal recording. These devices constitute portable systems, and they are functionally wearable [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]. The origins of the advent of Network Centric Therapy commence with the research, development, testing, and evaluation for quantifying reflex response and latency, which subsequently lead to the extrapolation to the domains of wearable and wireless inertial sensors for gait and movement disorder quantification.
The global evolutionary pathway for Network Centric Therapy derives from the Ph.D. Dissertation research conducted by Dr. LeMoyne, which lead to the progressive development of a device known as the Wireless Quantified Reflex Device through the incremental develop of four generations. The preliminary success involved the quantification of reflex response through locally wireless accelerometers. In order to measure the response of the patellar tendon reflex, the wireless accelerometers were mounted proximal to the lateral malleolus, which signified their wearable capability [17, 18, 40].
The original wireless accelerometers were provided through internal UCLA research, and they were referred to as MedNodes. The MedNodes required specialized operation, as they were the scope of graduate-level research at UCLA. These wireless accelerometer nodes that were noted as conveniently wearable were applied to the first and second generations of the Wireless Quantified Reflex Device, and the quantification of the patellar tendon reflex was measured in an accurate and reliable manner. The collected signal data of the wireless accelerometer was transmitted to a locally situated computer for post-processing [55, 56]. Central to all four generations of the Wireless Quantified Reflex Device was the integration of a quantified potential energy impact pendulum to consistently evoke the patellar tendon reflex [17, 18, 39, 40, 55, 56].
The third and fourth generations of the Wireless Quantified Reflex Device included a second wireless accelerometer to determine the time of impact for the quantified potential energy impact pendulum. The first wireless accelerometer was mounted to the ankle to quantify reflex response and time of response. Using the temporal offset of the wireless accelerometer mounted on the impact pendulum evoking the patellar tendon reflex and the wireless accelerometer mounted about the lateral malleolus mounted about the ankle to measure reflex response, a functional patellar tendon reflex latency was derived. The third and fourth generations of the Wireless Quantified Reflex Device incorporated the G-link wireless accelerometer developed by Microstrain [17, 18, 39, 40].
The third generation Wireless Quantified Reflex Device utilized streaming signal data to the locally situated portable computer for acquisition of the accelerometer signal data and subsequent post-processing. The third generation Wireless Quantified Reflex Device was the first evolution to feature the ability to derive functional reflex latency through the tandem wireless accelerometers with one wireless accelerometer located on the potential energy impact pendulum that evokes the patellar tendon reflex and the other wireless accelerometer mounted proximal to the lateral malleolus of the ankle for also acquiring reflex response. The research findings demonstrated that patellar tendon reflex response and associated functional latency could be both quantified with considerable accuracy and reliability [39].
The observations of the third generation Wireless Quantified Reflex Device established opportunity for improvement, such as increasing the sampling rate for the tandem accelerometers. This improvement would implicate better acuity with respect to the derived functional latency of the patellar tendon reflex. An artificial reflex device was applied as intermediary before the development of the fourth generation Wireless Quantified Reflex Device. This intermediary device utilized the data logger of the G-link wireless accelerometers, which permitted augmented sampling rates, while retaining wireless connectivity to a local portable computer for accelerometer signal data downloading and post-processing [57, 58, 59].
The fourth generation Wireless Quantified Reflex Device successful applied a longitudinal study for multiple subjects. With the wireless accelerometer set to data logger configuration with subsequent wireless transmission, the Wireless Quantified Reflex Device successfully acquired patellar tendon reflex response and functional latency with considerable accuracy, reliability, and reproducibility [40]. Subsequent evolutions encompass the application of more robust wearable and wireless inertial sensor systems and conjunction with machine learning to distinguish a hemiplegic reflex pair regarding affected patellar tendon reflex and associated unaffected patellar tendon reflex [60, 61, 62].
The next improvement incorporated the use of the portable media device and smartphone for the quantification of reflex response as a functional wireless accelerometer platform using the potential energy impact pendulum to evoke the patellar tendon reflex [63, 64]. The portable media device was suited for facilities with local wireless internet zones [63]. For locations requiring broad telecommunication access, the smartphone provides better benefit [64]. Both applications feature a common software application that enables a discrete recording of the accelerometer signal for quantifying the reflex response, and the signal data can be attached to an email for wireless transmission to the Internet for post-processing anywhere in the world [63, 64].
For example, LeMoyne and Mastroianni conducted an experiment to quantify reflex response using a portable media device applying supramaximal stimulation of the patellar tendon reflex in Lhasa, Tibet of China. The signal data was wirelessly transmitted to the Internet as an email attachment, which served as a provisional Cloud computing resource. The data was later downloaded in Flagstaff, Arizona of the United States of America, which is effectively on the other side of the world, for post-processing [65].
Further advancements of the concept of quantifying reflex response, such as the patellar tendon, pertained to using the accelerometer signal, such as through a portable media device, to differentiate between a hemiplegic reflex pair. The hemiplegic affected leg’s patellar tendon reflex response is notably more amplified relative to the patellar tendon reflex response of the unaffected leg. By consolidating the respective accelerometer signals to a feature set for machine learning classification using the support vector machine available through the Waikato Environment for Knowledge Analysis (WEKA) considerable machine learning classification accuracy was attained [60]. This achievement is notable, since subjective clinical observations to distinguish between a hemiplegic reflex pair is a matter of contention [21].
The gyroscope was eventually incorporated in the inertial sensor package of portable media devices and smartphones. The gyroscope provides a clinical representation for rotational characteristics of a joint, which represents the response of the patellar tendon reflex. Successfully demonstration of the gyroscope to quantify the patellar tendon reflex was demonstrated in the context of the Wireless Quantified Reflex Device through the potential energy impact pendulum [61, 62, 66, 67, 68].
Using both the potential energy impact pendulum and supramaximal stimulation to evoke the patellar tendon reflex response multiple machine learning algorithms using WEKA have achieved considerable classification accuracy [60, 61, 62, 66, 67]. Figures 1 and 2 represent the gyroscope signal for the reflex response of the hemiplegic affected leg and unaffected leg, respectively. Machine learning algorithms, such as the J48 decision tree, provide a visualized basis for the most prevalent numeric attributes to establish classification accuracy, such as the time disparity between maximum and minimum angular rate of rotation for the patellar tendon reflex response, as illustrated in Figure 3 [67].
The gyroscope signal of the patellar tendon reflex response for the hemiplegic affected leg using the potential energy impact pendulum to evoke the reflex [
The gyroscope signal of the patellar tendon reflex response for the unaffected leg using the potential energy impact pendulum to evoke the reflex [
The J48 decision tree to distinguish between a hemiplegic affected leg and unaffected leg, for which the time disparity between maximum and minimum angular rate of rotation for the patellar tendon reflex response numeric attribute is illustrated as the most prevalent for establishing classification accuracy [
A readily noted capability observed by LeMoyne and Mastroianni was that since the wireless accelerometer was functionally wearable for the quantification of reflex response through mounting about the lateral malleolus of the patellar tendon, likewise the same mounting procedure could be applied for quantifying gait patterns [11, 12, 17]. Alternative mounting configurations, such as the lateral epicondyle proximal to the knee, were also feasible for assessing gait in a quantified context [11]. Additionally, the smartphone and portable media device were suitable candidates to represent functionally wearable and wireless inertial sensor systems (both accelerometers and gyroscopes) for gait quantification and eventually machine learning classification [3, 4, 5, 6, 7, 8, 9, 10]. These concepts were also applied to the quantification of movement disorders through the mounting of wearable and wireless inertial sensor systems about the dorsum of the hand [1, 2, 5, 6, 7, 8, 9, 10].
Preliminary attempts to apply functionally wearable wireless accelerometers to measure gait characteristics consisted of segmented subsystems and in some cases complex mounting techniques exceeding the knowledge of the common user [69, 70, 71, 72]. The highly miniaturized, portable, and non-intrusive nature of the G-link wireless accelerometer developed by Microstrain demonstrate its robust capability for quantifying gait characteristics [11]. Proof of concept from an engineering perspective was demonstrated for the identification of quantified disparity of hemiplegic gait and Virtual Proprioception to enable real-time rehabilitation of hemiplegic gait [73, 74, 75, 76].
Preliminary research, development, testing, and evaluation by LeMoyne et al. applied the G-link Microstrain wireless accelerometer to ascertain quantified disparity of hemiplegic gait. The wireless accelerometer nodes were effectively wearable. They could be mounted about the lateral epicondyle proximal to the knee through an elastic band or about the lateral malleolus near the ankle using the elastic band of a sock [74, 75].
The wireless accelerometer achieved connectivity to a locally situated personal computer, which would then serve as the basis for post-processing. Using the acceleration magnitude of the three-dimensional orthogonal acceleration signal, characteristic spikes of the acceleration magnitude signal represented the initiation of stance. The time averaged acceleration from stance to stance enabled the quantification of gait characteristics [74, 75]. Furthermore, through the ratio of the hemiplegic affected leg to the unaffected leg using the time averaged acceleration from stance to stance, the quantified disparity of hemiplegic gait could be quantified with the potential for deriving therapeutic intervention for rehabilitation [74]. Functionally wearable and locally wireless accelerometers have also been applied to successfully contrast hemiplegic gait with respect to the frequency domain [73]. Other applications of wireless accelerometer systems that are functionally wearable have been successfully demonstrated for the context of effectively autonomous gait analysis based on quantified data derived from the acceleration signal [11, 12, 69, 70, 71, 72].
Virtual Proprioception expanded the capabilities of functionally wearable wireless accelerometers for real-time modification of hemiplegic gait based on accelerometer signal data. The wireless accelerometers were mounted by flexible elastic bands proximal to the lateral epicondyle of the knee for both the unaffected leg and hemiplegic affected leg. Based on a visual feedback strategy the person with hemiplegic gait was able to modify the hemiplegic affected leg to a more representative acceleration signal representative of the unaffected leg [76].
During 2010 LeMoyne and Mastroianni sought to expand the availability of wearable and wireless accelerometer systems for quantifying gait in the context of more commercially available systems. The smartphone of that timeframe was equipped with an internal accelerometer. Additionally, the smartphone is inherently capable of wirelessly accessing the Internet. A software application for recording the accelerometer data for a prescribed duration and sampling rate with wireless transfer to the Internet as an email attachment enables the smartphone to function as a wearable and wireless inertial sensor system. The email resource represents a provisional representation of a Cloud computing resource. These characteristics enable the smartphone to quantify gait features in the context of a wearable and wireless inertial sensor system [77]. These preliminary capabilities constitute the origins of Network Centric Therapy for the domain of gait analysis [3, 4, 5, 6, 7, 78].
Preliminary testing and evaluation of the smartphone as a wearable and wireless inertial sensor system for gait analysis was conducted in region of Pittsburgh, Pennsylvania. The experimental gait analysis accelerometer data was conveyed wirelessly to the Internet as an email attachment for subsequent post-processing in the general area of Los Angeles, California. The implications were that experimental and post-processing locations could be geographically separated anywhere in the world with Internet connectivity [77].
The preliminary gait experiment of 2010 implementing the smartphone as a wearable and wireless inertial sensor system through the internal accelerometer involved mounting the smartphone proximal to the lateral malleolus of the ankle joint by an elastic band. Two primary gait characteristics were quantified, such as the temporal duration between stance to stance and time averaged acceleration from stance to stance. These parameters acquired by the smartphone functioning as a wearable and wireless inertial sensor system through the available accelerometer demonstrated considerable accuracy and reliability [77].
Additional and similar themed experiments pertained to quantification of gait through other mounting applications, which underscores the flexibility of the smartphone as a wearable and wireless inertial sensor system. The two other mounting positions involved the lateral epicondyle near the knee joint and lumbar-sacral aspect of the spine through an elastic band. The temporal duration between stance to stance cycle displayed considerable accuracy and reliability and successfully elucidated predominant frequencies in the context of the frequency domain with respect to both mounting strategies [79, 80].
Another device that is similar to the smartphone for applications as a wearable and wireless inertial sensor system for the quantification of gait is the portable media device. The portable media device can utilize the same software application as relevant to the smartphone. Although the portable media device is generally restricted to an area with local internet connectivity, this device has a lighter mass and is more affordable for tandem applications involving both legs for gait analysis [3, 4, 5, 6, 7, 8, 9, 10, 78].
Preliminary, testing of the portable media device was successfully demonstrated with mounting about the lateral malleolus of the leg by an elastic band. The accelerometer of the portable media device successfully quantified gait in an accurate and consistent manner. The experimental data was conveyed by wireless transmission through the Internet as an email attachment, and the experimental and post-processing resources were situated on opposite sides of the continental United States. Post-processing emphasized the derivation of step cycle time (stance to stance) and time averaged acceleration (stance to stance) [81].
An observation of the portable media device is that it is more affordable than the smartphone, such as for the application of two tandem operated portable media devices for quantifying the disparity of hemiplegic gait. LeMoyne and Mastroianni incorporated two portable media devices in the context of a wearable and wireless inertial sensor system, such as an accelerometer, for quantifying hemiplegic gait respective of the unaffected leg and the hemiplegic affected leg. The devices were mounted about the lateral malleolus of the ankle joint through an elastic band for both the unaffected leg and the hemiplegic affected leg. The tandem activated portable media devices successfully demonstrated the ability to quantitatively identify stance to stance temporal duration and stance to stance time averaged acceleration of the hemiplegic affected leg and unaffected leg with statistical significance. Also, the ratio of stance to stance time averaged acceleration less the offset for the hemiplegic affected leg and unaffected leg demonstrated quantified disparity [82].
Eventually a strategy for using a singular smartphone to quantify hemiplegic gait and its associated disparity was established with the incorporation of a treadmill to maintain constant velocity. The smartphone functioning as a wearable and wireless accelerometer platform was mounted about the lateral malleolus of the ankle by an elastic band. Automated post-processing software emphasized the rhythmic characteristics of gait and acquired gait parameters, such as stance to stance temporal disparity and stance to stance time averaged acceleration. The stance to stance temporal disparity did not display statistical significance, because of the treadmill velocity constraint. Statistical significance was achieved for stance to stance time averaged acceleration with respect to comparing the hemiplegic affected leg to the unaffected leg. This experimental configuration enables the evaluation and quantification of gait in an autonomous environment [83].
Evolutionary trends eventually enabled the smartphone to quantify gait through the internal gyroscope, which offers a more clinically representative kinematic signal. The strategy of conducting gait analysis constrained to a constant velocity by a treadmill was applied. A smartphone functioning as a wearable and wireless gyroscope platform quantified hemiplegic gait in terms of both the affected leg and unaffected leg with mounting about the lateral malleolus near the ankle joint through an elastic band. The gyroscope signal was consolidated to a feature set during the post-processing phase, which consisted of five numeric attributes: maximum, minimum, mean, standard deviation, and coefficient of variation. Using the multilayer perceptron neural network considerable classification accuracy was attained for distinguishing between the hemiplegic affected leg and unaffected leg during gait [84].
Additionally, the smartphone through its internal inertial sensor system has been applied to other applications pertaining to the domain of gait analysis and associated mobility. Smartphones have been successfully incorporated for augmenting the acuity of clinically standard evaluations, such as the Timed Up and Go and 6-Minute Walk Test [85, 86, 87]. An observed utility of the strategy of augmenting clinically standard evaluation techniques with functionally wearable and wireless inertial sensor systems, such as the smartphone, is the ability to evolve a clinical method rather than inventing a new methodology.
During this phase of the evolutionary process that lead to the realization of Network Centric Therapy a new observation occurred. Smartphones and portable media devices can function as representative and effective wearable and wireless inertial sensor systems. However, their evolutionary pathway is not consistent with the biomedical and healthcare domain. A new perspective for wearable and wireless inertial sensor systems was developed, which incorporated inertial sensor nodes with local wireless connectivity to a device, such as a smartphone or tablet, with considerably expanded wireless access to the Internet. This paradigm shift enabled considerable reduction in mass and volumetric profile for the wearable and wireless inertial sensor system. This strategy enabled segmented wireless access of the inertial signal data for connectivity to a Cloud computing resource [88].
During 2016 LeMoyne et al. utilized a wearable and wireless inertial sensor system architecture in the context of Network Centric Therapy for the evaluation of gait for subject’s with Friedreich’s ataxia. The system applied local wearable and wireless inertial sensor nodes with local wireless connectivity to a tablet with global wireless access to a Cloud computing environment. A multilayer perceptron neural network achieved considerable classification accuracy to distinguish between a person with healthy gait and gait for a person with Friedreich’s ataxia [89].
The current state of the art for demonstrating the capability of Network Centric Therapy involves the recent test and evaluation of the BioStamp nPoint, which represents a conformal wearable and wireless inertial sensor system. The BioStamp nPoint achieves wireless connectivity for acquiring signal data for quantifying gait in a segmented manner through wireless systems, such as a tablet for operation and smartphone for Cloud computing access. Figure 4 presents the supporting apparatus for the BioStamp nPoint conformal wearable and wireless inertial sensor system [90].
The BioStamp nPoint conformal wearable and wireless inertial sensor system and supporting devices, such as docking station, tablet, and smartphone [
Recently, during 2020 LeMoyne and Mastroianni applied the BioStamp nPoint to quantify hemiplegic gait with distinction through machine learning. The BioStamp nPoint conformal wearable and wireless inertial sensor system was mounted by adhesive medium to both the hemiplegic affected leg and unaffected leg about the femur and proximal to the patella as shown in Figure 5. The subject walked on a treadmill for the experiment [91].
The BioStamp nPoint conformal wearable and wireless inertial sensor system mounted about the femur for the quantification of hemiplegic gait [
The gyroscope signal revealed notable disparity respective of the affected leg and unaffected leg during gait as presented in Figures 6 and 7 respectively. Post-processing of the gyroscope signal data consolidated a feature set consisting of five numeric attributes based on descriptive statistics, such as maximum, minimum, mean, standard deviation, and coefficient of variation. Multiple machine learning classification algorithms, such as the support vector machine and multilayer perceptron neural network, achieved considerable classification accuracy to distinguish between the hemiplegic affected leg and unaffected leg [91, 92].
The BioStamp nPoint conformal wearable and wireless inertial sensor signal for the hemiplegic affected leg [
The BioStamp nPoint conformal wearable and wireless inertial sensor signal for the unaffected leg [
Functionally wearable accelerometer systems have been demonstrated for the quantification of movement disorder and also their response to intervention strategy [11, 12, 93, 94, 95, 96, 97, 98]. With the evolution of wireless technology other traditional inertial signal data transfer strategies have become effectively obsolete [99]. Intuitively, the G-link wireless accelerometer was a candidate for testing and evaluating the quantification of tremor associated with movement disorders [11, 12, 100, 101, 102, 103].
Preliminary demonstration of the G-link wireless accelerometer showed the ability to quantify simulated Parkinson’s disease hand tremor by mounting the device to the dorsum of the hand [100, 101]. Eventually simulated Parkinson’s disease tremor was contrasted to a static condition. Post-processing of the signal data involved the time averaged acceleration, for which statistical significance was achieved [100]. A similar wireless inertial sensor system configuration was successfully demonstrated for the quantification of Parkinson’s disease hand tremor within this timeframe [104].
LeMoyne and Mastroianni during 2010 extended the capability of wearable and wireless inertial sensor systems for quantifying Parkinson disease hand tremor through the application of a smartphone. A software application enabled the smartphone to quantify hand tremor for a prescribed temporal duration through the smartphone’s internal accelerometer. The accelerometer signal data was conveyed by wireless connectivity to the Internet as an email attachment. Statistical significance was achieved with respect to the subject with Parkinson’s disease hand tremor and subject without Parkinson disease. Notably, the experiment occurred in metropolitan Pittsburgh, Pennsylvania and the post-processing was conducted in the general area of Los Angeles, California [105]. The research team observed that experimental and post-processing resources could be geographically separated anywhere in the world with Internet access [1, 2, 5, 6, 7, 8, 9, 10, 105, 106]. This observation constitutes the origins of Network Centric Therapy with regards to movement disorders [1, 2, 5, 6, 7, 106].
Using the smartphone as an inertial sensor platform with wearable properties the recorded signal data can represent instrumental feedback with respect to the efficacy of therapy response. For example, with machine learning classification the smartphone functioning as a wearable and wireless inertial sensor platform can distinguish between deep brain stimulation set to ‘On’ and ‘Off’ status. A person with Essential tremor performed a reach and grasp task with a smartphone mounted to the dorsum of the hand by a latex glove. Post-processing consolidated the inertial signal data to a feature set amenable for machine learning classification, and considerable classification accuracy was achieved through the application of a support vector machine to differentiate between deep brain stimulation set to ‘On’ and ‘Off’ status [107]. In conjunction with the preliminary success of the research with respect to Essential tremor and deep brain stimulation set to ‘On’ and ‘Off’ status the multilayer perceptron neural network also attained considerable machine learning classification accuracy for differentiating these deep brain stimulation settings [108].
Another extrapolation of this research perspective involved considering six machine learning algorithms: multilayer perceptron neural network, support vector machine, K-nearest neighbors, logistic regression, J48 decision tree, and random forest. The reach and grasp task was applied for a subject with Essential tremor treated by deep brain stimulation with respect to ‘On’ and ‘Off’ status. Three feature set scenarios were addressed to determine the most appropriate machine learning algorithms: accelerometer and gyroscope signal recordings, accelerometer signal recordings, and gyroscope signal recordings. The multilayer perceptron neural network, support vector machine, K-nearest neighbors, and logistic regression achieved the highest classification accuracy in consideration of these three feature set scenarios [109].
The accelerometer and gyroscope intrinsic to the smartphone was also applied for the evaluation of deep brain stimulation efficacy for the treatment of Parkinson’s disease. Deep brain stimulation was set to ‘On’ and ‘Off’ status with the hand tremor response measured by a smartphone mounted to the dorsum of the hand through a latex glove. Multiple machine learning algorithms were evaluated: multilayer perceptron neural network, support vector machine, K-nearest neighbors, logistic regression, J48 decision tree, and random forest. The feature set consisted of descriptive statistics for both the accelerometer and gyroscope signal data. Two performance parameters were considered, such as classification accuracy and time to develop the machine learning model. The support vector machine and logistic regression best satisfied these two performance parameters [110]. The multilayer perceptron neural network achieved considerable classification accuracy to distinguish between the deep brain stimulation set to ‘On’ and ‘Off’ status for Parkinson’s disease hand tremor, but the time to develop the model was considerably protracted [110, 111].
Network Centric Therapy was further realized for the domain of movement disorders through the BioStamp nPoint. The BioStamp nPoint is a conformal wearable and wireless inertial sensor system with segmented operation and wireless transmission of signal data to a secure Cloud computing environment with wireless connectivity to a smartphone and tablet. The conformal sensors also have a mass less than ten grams and a profile on the order of a bandage. Additionally, the BioStamp nPoint is certified as an FDA 510(k) medical device for the acquisition of medical grade data [5, 90]. These attributes of the BioStamp nPoint ideally accommodate the quantification of movement disorder tremor response, such as for Parkinson’s disease, based on deep brain stimulation intervention through mounting about the dorsum of the hand using an adhesive medium as illustrated in Figure 8 [112].
The BioStamp nPoint conformal wearable and wireless inertial sensor system mounted about the dorsum of the hand for quantifying movement disorder tremor response, such as for Parkinson’s disease, as a result of deep brain stimulation intervention [
Multiple sets of deep brain stimulation parameter configurations have been evaluated for the treatment of Parkinson’s disease using the BioStamp nPoint to quantify the response and machine learning to distinguish the respective parameter configurations [112, 113, 114, 115]. The BioStamp nPoint was mounted to the dorsum of the hand through an adhesive medium. The deep brain stimulation amplitude was evaluated at multiple settings, such as ‘Off’ status as a baseline, amplitude set to 1.0 mA, 2.5 mA, and 4.0 mA. The acceleration signal derived from the BioStamp nPoint conformal wearable and wireless inertial sensor system was post-processed to present the acceleration magnitude as illustrated in Figures 9–12 [112].
Acceleration magnitude derived from the BioStamp nPoint conformal wearable and wireless inertial sensor system for hand tremor from a subject with Parkinson’s disease with deep brain stimulation set to ‘Off’ status [
Acceleration magnitude derived from the BioStamp nPoint conformal wearable and wireless inertial sensor system for hand tremor from a subject with Parkinson’s disease with deep brain stimulation set to amplitude equal to 1.0 mA [
Acceleration magnitude derived from the BioStamp nPoint conformal wearable and wireless inertial sensor system for hand tremor from a subject with Parkinson’s disease with deep brain stimulation set to amplitude equal to 2.5 mA [
Acceleration magnitude derived from the BioStamp nPoint conformal wearable and wireless inertial sensor system for hand tremor from a subject with Parkinson’s disease with deep brain stimulation set to amplitude equal to 4.0 mA [
The acceleration magnitude signal data was consolidated to a feature set though Python. The feature set was composed of numeric attributes, such as maximum, minimum, mean, standard deviation, and coefficient of variation. Machine learning algorithms, such as J48 decision tree, K-nearest neighbors, support vector machine, logistic regression, and random forest were contrasted in terms of their classification accuracy and time to develop the machine learning model. Based on these criteria the K-nearest neighbors machine learning algorithm displayed the optimal satisfaction of classification accuracy in conjunction with time to develop the machine learning model and the support vector machine achieved the optimal classification accuracy [112]. The multilayer perceptron neural network also demonstrated considerable classification accuracy [113].
Deep learning was then applied to distinguish between deep brain stimulation parameter configuration settings for the treatment of Parkinson’s disease, such as ‘Off’ status as a baseline, amplitude set to 1.0 mA, amplitude set to 1.75 mA, amplitude set to 2.5 mA, amplitude set to 3.25 mA, and amplitude set to 4.0 mA. The BioStamp nPoint conformal wearable and wireless inertial sensor system provided the accelerometer signal data. The post-processing was facilitated by Google Colab and TensorFlow to implement a convolutional neural network. The convolutional neural network achieved considerable classification accuracy to distinguish between all six of these parameter configurations [116, 117].
Network Centric Therapy is anticipated to have a transformative influence on the healthcare and biomedical industry. Conformal wearable and wireless inertial sensor systems are envisioned to enable historical and distinctly quantified data for subjects undergoing rehabilitation and subjects with neurodegenerative movement disorders, such as Parkinson’s disease and Essential tremor. Data science methodologies can be incorporated to optimize the respective therapy strategy. With the amalgamation of machine learning and eventually deep learning conformal wearable and wireless inertial sensor systems are predicted to considerably advance augmented clinical situational awareness for diagnostic and prognostic capabilities. In particular, with the Cloud computing accessibility intrinsic to Network Centric Therapy, the most talented clinical resources from anywhere in the world can provide optimal patient specific rehabilitation and therapy to subjects from the convenience of a homebound setting. Additionally, the inherent aspects of Network Centric Therapy, such as conformal wearable and wireless inertial sensor systems, machine learning, and Cloud computing access, imply a plausible pathway to the closed-loop optimization of deep brain stimulation parameter configurations.
The evolutionary perspective for the advent of Network Centric Therapy for the domains of assessing reflex, gait, and movement disorders have been thoroughly discussed. Inherent aspects pertaining to Network Centric Therapy involve wearable and wireless inertial sensor systems, machine learning, and Cloud computing access for the acquired inertial sensor signal data. The implications are that expert clinicians can access a patient’s health status based on the wearable and wireless inertial sensor system signal data from anywhere in the world. These achievements constitute a significant evolution relative to traditional ordinal scale methodologies and electro-mechanical signal data obtained by clinical laboratory resources. Conformal wearable and wireless inertial sensor systems have further evolved the capabilities of Network Centric Therapy. In the future Network Centric Therapy is envisioned to augment clinical diagnostic and prognostic acuity, optimize rehabilitation, and enable closed-loop optimization of deep brain stimulation parameter configurations.
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Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"May 18th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:4,numberOfPublishedChapters:287,numberOfPublishedBooks:27,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},subseries:[{id:"14",title:"Cell and Molecular Biology",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression",scope:"The Cell and Molecular Biology topic within the IntechOpen Biochemistry Series aims to rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",annualVolume:11411,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,editorialBoard:[{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},{id:"17",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRqB9QAK/Profile_Picture_1626163237970",institutionString:null,institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"chapter.detail",path:"/chapters/14013",hash:"",query:{},params:{id:"14013"},fullPath:"/chapters/14013",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()