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Applied Software Development With Python Machine Learning By Wearable Wireless Systems For Movement Disorder Treatment Via Deep Brain Stimulation
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Book Synopsis Applied Software Development With Python & Machine Learning By Wearable & Wireless Systems For Movement Disorder Treatment Via Deep Brain Stimulation by : Robert Lemoyne
Download or read book Applied Software Development With Python & Machine Learning By Wearable & Wireless Systems For Movement Disorder Treatment Via Deep Brain Stimulation written by Robert Lemoyne and published by World Scientific. This book was released on 2021-08-26 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book presents the confluence of wearable and wireless inertial sensor systems, such as a smartphone, for deep brain stimulation for treating movement disorders, such as essential tremor, and machine learning. The machine learning distinguishes between distinct deep brain stimulation settings, such as 'On' and 'Off' status. This achievement demonstrates preliminary insight with respect to the concept of Network Centric Therapy, which essentially represents the Internet of Things for healthcare and the biomedical industry, inclusive of wearable and wireless inertial sensor systems, machine learning, and access to Cloud computing resources.Imperative to the realization of these objectives is the organization of the software development process. Requirements and pseudo code are derived, and software automation using Python for post-processing the inertial sensor signal data to a feature set for machine learning is progressively developed. A perspective of machine learning in terms of a conceptual basis and operational overview is provided. Subsequently, an assortment of machine learning algorithms is evaluated based on quantification of a reach and grasp task for essential tremor using a smartphone as a wearable and wireless accelerometer system.Furthermore, these skills regarding the software development process and machine learning applications with wearable and wireless inertial sensor systems enable new and novel biomedical research only bounded by the reader's creativity.Related Link(s)
Book Synopsis Applied Software Development With Python & Machine Learning By Wearable & Wireless Systems For Movement Disorder Treatment Via Deep Brain Stimulation by : Robert Charles LeMoyne
Download or read book Applied Software Development With Python & Machine Learning By Wearable & Wireless Systems For Movement Disorder Treatment Via Deep Brain Stimulation written by Robert Charles LeMoyne and published by . This book was released on 2021 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The book presents the confluence of wearable and wireless inertial sensor systems, such as a smartphone, for deep brain stimulation for treating movement disorders, such as essential tremor, and machine learning. The machine learning distinguishes between distinct deep brain stimulation settings, such as 'On' and 'Off' status. This achievement demonstrates preliminary insight with respect to the concept of Network Centric Therapy, which essentially represents the Internet of Things for healthcare and the biomedical industry, inclusive of wearable and wireless inertial sensor systems, machine learning, and access to Cloud computing resources. Imperative to the realization of these objectives is the organization of the software development process. Requirements and pseudo code are derived, and software automation using Python for post-processing the inertial sensor signal data to a feature set for machine learning is progressively developed. A perspective of machine learning in terms of a conceptual basis and operational overview is provided. Subsequently, an assortment of machine learning algorithms is evaluated based on quantification of a reach and grasp task for essential tremor using a smartphone as a wearable and wireless accelerometer system. Furthermore, these skills regarding the software development process and machine learning applications with wearable and wireless inertial sensor systems enable new and novel biomedical research only bounded by the reader's creativity"--
Book Synopsis Wearable and Wireless Systems for Healthcare II by : Robert LeMoyne
Download or read book Wearable and Wireless Systems for Healthcare II written by Robert LeMoyne and published by Springer Nature. This book was released on with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Wearable and Wireless Systems for Healthcare II by : Robert LeMoyne
Download or read book Wearable and Wireless Systems for Healthcare II written by Robert LeMoyne and published by Springer. This book was released on 2019-02-20 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a far-sighted perspective on the role of wearable and wireless systems for movement disorder evaluation, such as Parkinson’s disease and Essential tremor. These observations are brought together in the application of quantified feedback for deep brain stimulation systems using the wireless accelerometer and gyroscope of a smartphone to determine tuning efficacy. The perspective of the book ranges from the pioneering application of these devices, such as the smartphone, for quantifying Parkinson’s disease and Essential tremor characteristics, to the current state of the art. Dr. LeMoyne has published multiple first-of-their-kind applications using smartphones to quantify movement disorder, with associated extrapolation to portable media devices.
Book Synopsis Wearable and Wireless Systems for Healthcare I by : Robert Charles LeMoyne
Download or read book Wearable and Wireless Systems for Healthcare I written by Robert Charles LeMoyne and published by Springer Nature. This book was released on 2024 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the second edition of the one originally published in 2017. The original publication features the discovery of numerous novel applications for the use of smartphones and portable media devices for the quantification of gait, reflex response, and an assortment of other concepts that constitute first-in-the-world applications for these devices. Since the first edition, numerous evolutions involving the domain of wearable and wireless systems for healthcare have transpired warranting the publication of the second edition. This volume covers wearable and wireless systems for healthcare that are far more oriented to the unique requirements of the biomedical domain. The paradigm-shifting new wearables have been successfully applied to gait analysis, homebound therapy, and quantifiable exercise. Additionally, the confluence of wearable and wireless systems for healthcare with deep learning and neuromorphic applications for classification is addressed. The authors expect that these significant developments make this book valuable for all readers.
Book Synopsis Machine Learning to Optimize Embedded Adaptive Deep Brain Stimulation by : Benjamin Isaac Ferleger
Download or read book Machine Learning to Optimize Embedded Adaptive Deep Brain Stimulation written by Benjamin Isaac Ferleger and published by . This book was released on 2020 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis focuses on the development and application of novel machine learning approaches to the problem of optimization in adaptive deep brain stimulation. As brain-computer and brain-machine interfacing has rapidly developed in the past few years, attention in the relevant research has shifted from proof-of-concept to proof-of-feasibility. One of the first and, therefore, best-developed neurotechnologies is deep brain stimulation (DBS). DBS is a surgical intervention prescribed for several treatment-refractory neurological conditions. First, a stimulating electrode is chronically implanted into a condition-specific deep brain structure. The parameters of the stimulation provided by this electrode are then set bya clinician. Stimulation remains at these parameters continuously unless a patient actively chooses to disable their treatment. As has been repeatedly demonstrated, DBS is a safe and effective treatment for a number of movement disorders and is under active investigation for potential use in several psychiatric conditions.DBS, however, is not a panacea. Battery replacement requires a revision surgery, and even rechargeable systems0́9 batteries must generally be replaced at least once within a device's lifetime. Additionally, DBS therapy is associated with a number of unpleasant and sometimes dangerous side effects. These side effects can range from transient paresthesias to episodes of depression and mania, and are broadly correlated with high levels of stimulation over long periods of time. In addition to concerns over battery life and side effects, the programming procedure for DBS is based primarily on a back-and-forth between clinicians and patients in a clinical setting. If we define "optimal" treatment as the most complete suppression of symptoms with the least manifestation of side effects, then achieving the corresponding settings is the goal of this procedure. The time consuming nature of this procedure, when considered in the context of clinical time constraints and patient fatigue, means that the parameters selected are far more likely to be the first passable setting than the truly optimal one. Patients are generally given the ability to disable their stimulation or select from a small range of amplitudes, but cannot actively reprogram their devices outside of a clinical setting. One technique with the potential to alleviate concerns about side effects and battery life is adaptive deep brain stimulation (aDBS). aDBS refers to any method that uses feedback on a patient's state to modulate stimulation parameters in real time. This feedback could come in the form of gyroscope and accelerometer data in the case of movement disorders, or could be derived from neural signals that are correlated with the onset of symptoms. These signals are then processed and meaningful features extracted from them, which may in turn be used to determine the appropriate stimulation parameters. This ensures that stimulation is only applied as needed. It is important to note that aDBS systems also intrinsically expand the state space forDBS programming, potentially adding yet more complexity to an already laborious procedure. As directional leads become more common in DBS devices, this expanded programming state space further reduces the likelihood of optimal settings being reached. A twin requirement to developing effective aDBS systems is thus the design of a streamlined procedure for parameter optimization in DBS programming. This may be accomplished through the introduction of an automated programming pipeline. Through the collection of quantified data on symptom severity through the use of gyroscope or accelerometer data and the digitization of patient feedback on side effects, this pipeline could considerably speed testing.In addition, the digitization of the data required to analyze aDBS parameter performance could be integrated with modern optimization techniques, such that a personalized optimal treatment may be determined to within an increased degree of certainty. This work details approaches for resolving these deeply interwoven problems in aDBS treatment through insights from the fields of machine learning and optimization. We begin by considering the current state of the art in adaptive deep brain stimulation, its accomplishments, but especially its limitations. The principal limitations are: a general reliance on distributed systems that hinder free movement in patients; a focus solely on computationally inexpensive, but potentially suboptimal, binary aDBS control strategies; and the lack of an effective pipeline to deploy optimization methods during or after programming. Furthermore, recognition that these limitations are fundamentally interrelated implies that an integrated approach is required. The three key developments detailed in this work are thus themselves closely interrelated.Despite minor reductions in power savings, fully embedded binary aDBS is specifically de-signed to maximize therapeutic efficacy and ease of programming. Our results demonstrate that such a system is prepared for widespread studies in movement disorders. Our graded aDBS system yielded inconclusive results with regards to power savings and therapeutic efficacy. However, basing our approach to feature selection for symptom estimation from neural data on a model-free foundation has yielded promising evidence for relying on data-driven feature extraction. This approach to feature extraction intrinsically requires less direct programming, and instead maximizes the insights that may be gained from the data itself. Our development of a pipeline for automated programming of DBS parameters based on inertial measurements and patient feedback on side effects was designed to generalize easily into future integration with aDBS programming procedures. Finally, our computational approach to extracting information from a tablet- and mobile-based application demonstrates that semi- or fully-automated remote symptom assessment has the potential to significantly improve the future delivery of optimized, individualized treatment. Throughout this work, integration is a key component of discussion and consideration. Each result extracted from the developments discussed herein represents a small step away from the current standard of care. Considered individually, these steps would be taken incompletely different directions. It is the hope of the author that this work instead constitutes a realignment of these disparate goals and an explicit recognition of their inter-relatedness. Only by treating these problems as different faces of the same die can we arrive at truly optimized personalized treatment in aDBS.
Book Synopsis Wearable Telemedicine Technology for the Healthcare Industry by : Deepak Gupta
Download or read book Wearable Telemedicine Technology for the Healthcare Industry written by Deepak Gupta and published by Academic Press. This book was released on 2021-11-16 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Wearable Telemedicine Technology for the Healthcare Industry: Product Design and Development focuses on recent advances and benefits of wearable telemedicine techniques for remote health monitoring and prevention of chronic conditions, providing real time feedback and help with rehabilitation and biomedical applications. Readers will learn about various techniques used by software engineers, computer scientists and biomedical engineers to apply intelligent systems, artificial intelligence, machine learning, virtual reality and augmented reality to gather, transmit, analyze and deliver real-time clinical and biological data to clinicians, patients and researchers. Wearable telemedicine technology is currently establishing its place with large-scale impact in many healthcare sectors because information about patient health conditions can be gathered anytime and anywhere outside of traditional clinical settings, hence saving time, money and even lives. - Provides readers with methods and applications for wearable devices for ubiquitous health and activity monitoring, wearable biosensors, wearable app development and management using machine learning techniques, and more - Integrates coverage of a number of key wearable technologies, such as ubiquitous textile systems for movement disorders, remote surgery using telemedicine, intelligent computing algorithms for smart wearable healthcare devices, blockchain, and more - Provides readers with in-depth coverage of wearable product design and development
Book Synopsis A Wearable Platform for Decoding Single-Neuron and Local Field Potential Activity in Freely-Moving Humans by : Uros Topalovic
Download or read book A Wearable Platform for Decoding Single-Neuron and Local Field Potential Activity in Freely-Moving Humans written by Uros Topalovic and published by . This book was released on 2022 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in technologies that can record and stimulate deep-brain activity in humans have led to impactful discoveries within the field of neuroscience and contributed to the develop- ment of novel closed-loop stimulation therapies for neurological and psychiatric disorders. Human neuroscience research based on intracranial electroencephalography (iEEG) is con- ducted on voluntary basis during various stages of participant's disease treatment using both external (in-clinic) and implantable systems. In clinical practice, external systems serve as monitoring and testing ground for biomarker extraction and closed-loop neuromodulation, which are, once approved, translated into a compact and low compute resource implantable version for disorder treatment. External systems allow recordings with fine spatiotemporal resolution at the expense of participant's mobility due to their large size, while implantable devices have reduced record- ing capabilities and they are not restricted to clinical environment. Due to high transmission and processing latencies across multiple devices, external systems have limited support for testing computationally expensive online biomarker detection and machine-learning based closed-loop electrical stimulation paradigms including online stimulation programmability. The motivation for this work comes from the need to extend capabilities of externalized systems, allowing more naturalistic (freely-moving) human neuroscience experiments with fine spatiotemporal resolution. Additionally, externalized systems should provide flexible and local hardware resources that can support real-time and moderately complex embedded neural decoders (biomarker extraction), which in turn could be used to trigger adaptive closed-loop stimulation with low latency. In order to demonstrate initial proof-of-concept technology, this work incorporates: 1. A small versatile neuromodulation platform that can be wearable and lightweight, supporting up to 16 depth electrode arrays; 2. A high-rate (" MB/s on all channels) interfacing of the analog sensing and stimulation front-ends with wearable hardware suitable for embedded machine learning algorithms including artificial neural networks (usually100M multi-accumulate operations or MACs); 3. A state of the art, performance-driven, neural decoder, small enough to run on an embedded hardware and large enough to generalize across participants; 4. Real-time training and inference with millisecond latency; 5. Closing the loop from the decoder output to the stimulation engines. Therefore, we developed a wearable, miniaturized, embedded, and external neuromodula- tion platform built from previously reported integrated circuits for sensing and stimulation, and interfaced with Edge Tensor Processing Unit (TPU) for real-time neural analysis. The Neuro-stack can record and decode single-neuron (32 channels), local field potential (LFP; 256 channels) activity, and deliver highly programmable current-controlled stimulation (256 channels) during stationary and ambulatory behaviors in humans. The TPU Dev Board was chosen because of the ability to perform 2 trillion MACs per second (64 64 MAC matrix at 480 MHz) using 2 W of power, with data bandwidth of 40 MB/s. Additionally, the system contains a field-programmable gate array (FPGA) for data pre-processing (filtering, down-sampling) and ARM-based microprocessor (TPU Dev Board) for data management, device control, and secure wireless access point. The Neuro-stack interfaces with the brain through commonly used macro- and micro-electrodes. The Neuro-stack validation includes in-vitro testing of recorded signal quality and measurement of system induced delays (e.g., closed-loop delay from sensing to stimulation site - 1.57 0.19 ms). We provide in-vivo single-unit, LFP, iEEG, and stimulation delivery recorded (2 - 40 channels) from twelve hu- man participants who had depth electrodes implanted for epilepsy evaluation. Among this data are also the first recordings of single-neuron activity during human walking. To utilize hardware capabilities of the Neuro-stack, we developed a software decoder based on prerecorded human LFP data, which uses TensorFlow artificial neural network (sequential convolutional 1D and recurrent layers) to predict the outcome of a memory task from raw data with higher performance (F1-score 88.6 5.5%) than current state of the art that use shallow machine learning methods (
Book Synopsis An Evolutionary Perspective for Network Centric Therapy Through Wearable and Wireless Systems for Reflex, Gait, and Movement Disorder Assessment with Machine Learning by : Robert LeMoyne
Download or read book An Evolutionary Perspective for Network Centric Therapy Through Wearable and Wireless Systems for Reflex, Gait, and Movement Disorder Assessment with Machine Learning written by Robert LeMoyne and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Wearable and wireless systems have progressively evolved to achieve the capabilities of Network Centric Therapy. Network Centric Therapy comprises the application of wearable and wireless inertial sensors for the quantification of human movement, such as reflex response, gait, and movement disorders, with machine learning classification representing advanced diagnostics. With wireless access to a functional Cloud computing environment Network Centric Therapy enables subjects to be evaluated at any location of choice with Internet connectivity and expert medical post-processing resources situated anywhere in the world. The evolutionary origins leading to the presence of Network Centric Therapy are detailed. With the historical perspective and state of the art presented, future concepts are addressed.
Download or read book Digital Health written by Alan Godfrey and published by Academic Press. This book was released on 2021-07-06 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Digital Health: Exploring Use and Integration of Wearables is the first book to show how and why engineering theory is used to solve real-world clinical applications, considering the knowledge and lessons gathered during many international projects. This book provides a pragmatic A to Z guide on the design, deployment and use of wearable technologies for laboratory and remote patient assessment, aligning the shared interests of diverse professions to meet with a common goal of translating engineering theory to modern clinical practice. It offers multidisciplinary experiences to guide engineers where no clinically advice and expertise may be available. Entering the domain of wearables in healthcare is notoriously difficult as projects and ideas often fail to deliver due to the lack of clinical understanding, i.e., what do healthcare professionals and patients really need? This book provides engineers and computer scientists with the clinical guidance to ensure their novel work successfully translates to inform real-world clinical diagnosis, treatment and management. - Presents the first guide for wearable technologies in a multidisciplinary and translational manner - Helps engineers design real-world applications to help them better understand theory and drive pragmatic clinical solutions - Combines the expertise of engineers and clinicians in one go-to guide, accessible to all
Book Synopsis Low-power Wearable Healthcare Sensors by : R. Simon Sherratt
Download or read book Low-power Wearable Healthcare Sensors written by R. Simon Sherratt and published by MDPI. This book was released on 2020-12-29 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in technology have produced a range of on-body sensors and smartwatches that can be used to monitor a wearer’s health with the objective to keep the user healthy. However, the real potential of such devices not only lies in monitoring but also in interactive communication with expert-system-based cloud services to offer personalized and real-time healthcare advice that will enable the user to manage their health and, over time, to reduce expensive hospital admissions. To meet this goal, the research challenges for the next generation of wearable healthcare devices include the need to offer a wide range of sensing, computing, communication, and human–computer interaction methods, all within a tiny device with limited resources and electrical power. This Special Issue presents a collection of six papers on a wide range of research developments that highlight the specific challenges in creating the next generation of low-power wearable healthcare sensors.
Download or read book Blindsight written by Peter Watts and published by Macmillan. This book was released on 2006-10-03 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hugo and Shirley Jackson award-winning Peter Watts stands on the cutting edge of hard SF with his acclaimed novel, Blindsight Two months since the stars fell... Two months of silence, while a world held its breath. Now some half-derelict space probe, sparking fitfully past Neptune's orbit, hears a whisper from the edge of the solar system: a faint signal sweeping the cosmos like a lighthouse beam. Whatever's out there isn't talking to us. It's talking to some distant star, perhaps. Or perhaps to something closer, something en route. So who do you send to force introductions with unknown and unknowable alien intellect that doesn't wish to be met? You send a linguist with multiple personalities, her brain surgically partitioned into separate, sentient processing cores. You send a biologist so radically interfaced with machinery that he sees x-rays and tastes ultrasound. You send a pacifist warrior in the faint hope she won't be needed. You send a monster to command them all, an extinct hominid predator once called vampire, recalled from the grave with the voodoo of recombinant genetics and the blood of sociopaths. And you send a synthesist—an informational topologist with half his mind gone—as an interface between here and there. Pray they can be trusted with the fate of a world. They may be more alien than the thing they've been sent to find. At the Publisher's request, this title is being sold without Digital Rights Management Software (DRM) applied.
Book Synopsis Handbook of Large-Scale Distributed Computing in Smart Healthcare by : Samee U. Khan
Download or read book Handbook of Large-Scale Distributed Computing in Smart Healthcare written by Samee U. Khan and published by Springer. This book was released on 2017-08-07 with total page 630 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume offers readers various perspectives and visions for cutting-edge research in ubiquitous healthcare. The topics emphasize large-scale architectures and high performance solutions for smart healthcare, healthcare monitoring using large-scale computing techniques, Internet of Things (IoT) and big data analytics for healthcare, Fog Computing, mobile health, large-scale medical data mining, advanced machine learning methods for mining multidimensional sensor data, smart homes, and resource allocation methods for the BANs. The book contains high quality chapters contributed by leading international researchers working in domains, such as e-Health, pervasive and context-aware computing, cloud, grid, cluster, and big-data computing. We are optimistic that the topics included in this book will provide a multidisciplinary research platform to the researchers, practitioners, and students from biomedical engineering, health informatics, computer science, and computer engineering.
Book Synopsis Wearable Technologies in Organizations by : Aleksandra Przegalinska
Download or read book Wearable Technologies in Organizations written by Aleksandra Przegalinska and published by Palgrave Pivot. This book was released on 2019-01-15 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This innovative book considers the positive and negative impact of wearable technologies on organization and work. First discussing the development and use of this software within the workspace, the author highlights potential issues such as privacy, addiction and lack of work efficiency. Technology has had a major impact on workspace and workforce, and the second section explores how it has emerged as a key driver of collaboration, and what the shortfalls are in terms of autonomy, solidarity and authenticity. Cloud technology, mobile technology, collaboration apps, the Internet of Things, and highly specialized AI bear the promise of a radical enhancement of the way we work and interact. This book discusses the potential future scenarios for wearable technologies in the context of the IoT and as a social and organizational phenomenon.
Book Synopsis Brain-Computer Interfaces by : Desney S. Tan
Download or read book Brain-Computer Interfaces written by Desney S. Tan and published by Springer Science & Business Media. This book was released on 2010-06-10 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: For generations, humans have fantasized about the ability to create devices that can see into a person’s mind and thoughts, or to communicate and interact with machines through thought alone. Such ideas have long captured the imagination of humankind in the form of ancient myths and modern science fiction stories. Recent advances in cognitive neuroscience and brain imaging technologies have started to turn these myths into a reality, and are providing us with the ability to interface directly with the human brain. This ability is made possible through the use of sensors that monitor physical processes within the brain which correspond with certain forms of thought. Brain-Computer Interfaces: Applying our Minds to Human-Computer Interaction broadly surveys research in the Brain-Computer Interface domain. More specifically, each chapter articulates some of the challenges and opportunities for using brain sensing in Human-Computer Interaction work, as well as applying Human-Computer Interaction solutions to brain sensing work. For researchers with little or no expertise in neuroscience or brain sensing, the book provides background information to equip them to not only appreciate the state-of-the-art, but also ideally to engage in novel research. For expert Brain-Computer Interface researchers, the book introduces ideas that can help in the quest to interpret intentional brain control and develop the ultimate input device. It challenges researchers to further explore passive brain sensing to evaluate interfaces and feed into adaptive computing systems. Most importantly, the book will connect multiple communities allowing research to leverage their work and expertise and blaze into the future.
Book Synopsis Advances in Signal Processing and Intelligent Recognition Systems by : Sabu M. Thampi
Download or read book Advances in Signal Processing and Intelligent Recognition Systems written by Sabu M. Thampi and published by Springer Nature. This book was released on 2021-02-06 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 6th International Symposium on Advances in Signal Processing and Intelligent Recognition Systems, SIRS 2020, held in Chennai, India, in October 2020. Due to the COVID-19 pandemic the conference was held online. The 22 revised full papers and 5 revised short papers presented were carefully reviewed and selected from 50 submissions. The papers cover wide research fields including information retrieval, human-computer interaction (HCI), information extraction, speech recognition.
Book Synopsis 8th European Medical and Biological Engineering Conference by : Tomaz Jarm
Download or read book 8th European Medical and Biological Engineering Conference written by Tomaz Jarm and published by Springer Nature. This book was released on 2020-11-29 with total page 1198 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims at informing on new trends, challenges and solutions, in the multidisciplinary field of biomedical engineering. It covers traditional biomedical engineering topics, as well as innovative applications such as artificial intelligence in health care, tissue engineering , neurotechnology and wearable devices. Further topics include mobile health and electroporation-based technologies, as well as new treatments in medicine. Gathering the proceedings of the 8th European Medical and Biological Engineering Conference (EMBEC 2020), held on November 29 - December 3, 2020, in Portorož, Slovenia, this book bridges fundamental and clinically-oriented research, emphasizing the role of education, translational research and commercialization of new ideas in biomedical engineering. It aims at inspiring and fostering communication and collaboration between engineers, physicists, biologists, physicians and other professionals dealing with cutting-edge themes in and advanced technologies serving the broad field of biomedical engineering.
