15 research outputs found

    The smart house for older persons and persons with physical disabilities: structure, technology arrangements, and perspectives

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    Intelligent Biosignal Processing in Wearable and Implantable Sensors

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    This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

    Smart Sensors for Healthcare and Medical Applications

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    This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue “Smart Sensors for Healthcare and Medical Applications”. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare

    Signal Processing Using Non-invasive Physiological Sensors

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    Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions

    Noninvasive Dynamic Characterization of Swallowing Kinematics and Impairments in High Resolution Cervical Auscultation via Deep Learning

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    Swallowing is a complex sensorimotor activity by which food and liquids are transferred from the oral cavity to the stomach. Swallowing requires the coordination between multiple subsystems which makes it subject to impairment secondary to a variety of medical or surgically related conditions. Dysphagia refers to any swallowing disorder and is common in patients with head and neck cancer and neurological conditions such as stroke. Dysphagia affects nearly 9 million adults and causes death for more than 60,000 yearly in the US. In this research, we utilize advanced signal processing techniques with sensor technology and deep learning methods to develop a noninvasive and widely available tool for the evaluation and diagnosis of swallowing problems. We investigate the use of modern spectral estimation methods in addition to convolutional recurrent neural networks to demarcate and localize the important swallowing physiological events that contribute to airway protection solely based on signals collected from non-invasive sensors attached to the anterior neck. These events include the full swallowing activity, upper esophageal sphincter opening duration and maximal opening diameter, and aspiration. We believe that combining sensor technology and state of the art deep learning architectures specialized in time series analysis, will help achieve great advances for dysphagia detection and management in terms of non-invasiveness, portability, and availability. Like never before, such advances will enable patients to get continuous feedback about their swallowing out of standard clinical care setting which will extremely facilitate their daily activities and enhance the quality of their lives

    Applications of EMG in Clinical and Sports Medicine

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    This second of two volumes on EMG (Electromyography) covers a wide range of clinical applications, as a complement to the methods discussed in volume 1. Topics range from gait and vibration analysis, through posture and falls prevention, to biofeedback in the treatment of neurologic swallowing impairment. The volume includes sections on back care, sports and performance medicine, gynecology/urology and orofacial function. Authors describe the procedures for their experimental studies with detailed and clear illustrations and references to the literature. The limitations of SEMG measures and methods for careful analysis are discussed. This broad compilation of articles discussing the use of EMG in both clinical and research applications demonstrates the utility of the method as a tool in a wide variety of disciplines and clinical fields

    Procedures and Methodologies for the Control and Improvement of Energy-Environmental Quality in Construction

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    This Special Issue aims at providing the state-of-the-art on procedures and methodologies developed to improve energy and environmental performance through building renovation. We are greatly thankful to our colleagues building physics experts, building technology researchers, and urban environment scholars who contributed to this Special Issue, for sharing their original works in the field

    Space Biology and Medicine

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    Volume IV is devoted to examining the medical and associated organizational measures used to maintain the health of space crews and to support their performance before, during, and after space flight. These measures, collectively known as the medical flight support system, are important contributors to the safety and success of space flight. The contributions of space hardware and the spacecraft environment to flight safety and mission success are covered in previous volumes of the Space Biology and Medicine series. In Volume IV, we address means of improving the reliability of people who are required to function in the unfamiliar environment of space flight as well as the importance of those who support the crew. Please note that the extensive collaboration between Russian and American teams for this volume of work resulted in a timeframe of publication longer than originally anticipated. Therefore, new research or insights may have emerged since the authors composed their chapters and references. This volume includes a list of authors' names and addresses should readers seek specifics on new information. At least three groups of factors act to perturb human physiological homeostasis during space flight. All have significant influence on health, psychological, and emotional status, tolerance, and work capacity. The first and most important of these factors is weightlessness, the most specific and radical change in the ambient environment; it causes a variety of functional and structural changes in human physiology. The second group of factors precludes the constraints associated with living in the sealed, confined environment of spacecraft. Although these factors are not unique to space flight, the limitations they entail in terms of an uncomfortable environment can diminish the well-being and performance of crewmembers in space. The third group of factors includes the occupational and social factors associated with the difficult, critical nature of the crewmembers' work: the risks involved in space flight, changes in circadian rhythms, and intragroup interactions. The physical and emotional stress and fatigue that develop under these conditions also can disturb human health and performance. In addition to these factors, the risk also exists that crewmembers will develop various illnesses during flight. The risk of illness is no less during space flight than on Earth, and may actually be greater for some classes of diseases

    Personality Identification from Social Media Using Deep Learning: A Review

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    Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed
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