Enabling Person-Centric Healthcare Using Ambient Assistive Technology
Springer International Publishing (Verlag)
978-3-031-38283-3 (ISBN)
This book experiences the future of patient-centered healthcare and dives into the latest advancements and transformative technologies that are revolutionizing the well-being of individuals around the globe. The readers can join authors on an engaging journey as the authors explore the captivating realm of ambient assisted living and unlock its immense potential for improving healthcare outcomes.
This book goes beyond mere exploration; it invites readers to embark on a voyage of discovery as authors unveil the outcomes of groundbreaking research ideas. With a diverse range of applications, from deep learning in healthcare to cutting-edge models, the authors offer a comprehensive view of the opportunities and challenges that lie ahead.
Whether you're a healthcare professional, an academic seeking the latest insights, or a researcher delving into the realms of ambient assistive technology, biomedical engineering, or computational intelligence, this book is an invaluable resource. Additionally, postgraduate students pursuing data engineering systems find it to be an essential guide.
Each chapter stands independently, providing a comprehensive overview of problem formulation and its tangible outcomes. The readers can immerse themselves in the world of patient-centered healthcare today and become part of the forefront of innovation.
Paolo Barsocchi is a senior researcher at the Information Science and Technologies Institute of the National Research Council in Pisa, Italy. In 2008, he was a visiting researcher at the Universitat Autònoma de Barcelona, Spain. Since 2017, he has been the Head of the Wireless Networks Research Laboratory. He is included in the World's Top 2% Scientists according to the Stanford University List in 2020 and 2021. His research interests are in the areas of the Internet of things, wireless sensor networks, cyber-physical systems, machine learning and data analysis techniques, smart environments, ambient assisted living, activity recognition, and indoor localization. He has been nominated as a regional competence reference person for advanced manufacturing solutions in Industry 4.0 in 2017, and as a contact person in the Cluster-PON call in 2017 for the CNR Department DIITET. He has been (and currently is) involved in several European projects, national and regional projects, in the following listed. The overall amount of attracted and managed funds both at European and national level is about EUR4M.
P Naga Srinivasu is an associate professor in the computer science and engineering department at Prasad V. Potluri Siddhartha Institute of Technology, India. He obtained his Bachelor's degree in computer science engineering from SSIET, JNTU Kakinada (2011), and a Masters's degree in Computer Science Technology from GITAM University, Visakhapatnam (2013). He was awarded a doctoral degree by GITAM university for his thesis on Automatic Segmentation Methods for Volumetric Estimate of damaged Areas in Astrocytoma instances Identified from the 2D Brain MR Imaging. His fields of study include biomedical imaging, soft computing, explainable AI, and healthcare informatics. He has published numerous publications in reputed peer-reviewed journals and has edited book volumes with various publishers like Springer, Elsevier, IGI Global, and Bentham Science. He was an active reviewer for more than 40 journals indexed in Scopus and Web of science. He also served as guest editor and technical advisory board member for various internationally reputed conferences.
Dr. A Garg is currently serving as Director, KIET Group of Institutions (Delhi-NCR), Ghaziabad. Dr. Garg holds his B.E. in Mechanical Engineering from Delhi Technological University in 1986 (erstwhile Delhi College of Engineering) and subsequently both M.Tech & Ph.D (Industrial Engineering) from IIT Delhi. He has also received an award of commendation for the innovation at the workplace from the Government in the year 2004. He's an accomplished engineering professional who's based out of Delhi and carrying 35+ years of experience in industry & academia which majorly comprises of working with different government & private organizations in various leadership roles. He has published several papers in International Journals, and his research areas are maintenance management, Supply chain management, Information systems, performance measurement, etc. As an academic leader, his focus has always been to create experienced engineers duly aligned with the needs of Industry 4.0.
AKASH KUMAR BHOI [B. Tech, M.Tech, Ph.D.] is listed in the World'sTop 2% of Scientists for single-year impact for the year 2022 (compiled byJohn P.A. Ioannidis, Stanford University & published by Elsevier BV) and currently associated with Directorate of Research, Sikkim Manipal University as Adjunct Research Faculty and also with the KIET Group of Institutions, India as Adjunct Faculty. He is also working as a Research Associate at Wireless Networks (WN) Research Laboratory, Institute of Information Science and Technologies, National Research Council (ISTI-CRN) Pisa, Italy. He was appointed as the honorary title of "Adjunct Fellow" Institute for Sustainable Industries & Liveable Cities (ISILC), Victoria University, Melbourne, Australia, for the period from 1 August 2021 to 31 July 2022. He
Sensor Datasets for Human Daily Safety and Well-being.- Habitpad: A Habit-Change Person-Centric Healthcare Mobile Application with Machine Leaning and Gamification Features for Obesity.- Human centered Mathematics: a framework for medical applications based on Extended Reality and Artificial Intelligence.- Attentive Vision-Based Model for Sarcopenia Screening by Automating Timed Up-and-Go (TUG) Test.- AAL with Deep Learning to classify the Diseases remotely from the image data.- Heart Failure Prediction Using Radial Basis with Metaheuristic Optimization.- Healthcare Management And Prediction Of Future Illness Through Autonomous Intelligent Advisory System Using Aat Computational Framework.- ResNet-50-CNN and LSTM based Arrhythmia detection model based on ECG dataset.- A Review of Brain-Computer Interface (BCI) System: Advancement and Applications.- Optimized TSA ResNet architecture with TSH -discriminatory features for kidney stone classification from QUS Images.- Ambient Healthcare: A new Paradigm in Medical Zone.- Illuminating Unexplored Corners in Healthcare Space using Ambience Intelligence.- Depression Assessment in Youths using an Enhanced Deep Learning Approach.- Telemedicine Enabled Remote Digital Healthcare System.
| Erscheinungsdatum | 20.09.2024 |
|---|---|
| Reihe/Serie | Studies in Computational Intelligence |
| Zusatzinfo | XII, 317 p. 113 illus., 87 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Medizin / Pharmazie ► Physiotherapie / Ergotherapie ► Orthopädie | |
| Technik | |
| Schlagworte | Ambient Assistive Living • Ambient Assistive Technology • biosensors • Computational Intelligence • Internet of Medical Things • Person-Centric Healthcare • Remote Electronic Healthcare Records • Remote Surveillance • Wearable technologies |
| ISBN-10 | 3-031-38283-8 / 3031382838 |
| ISBN-13 | 978-3-031-38283-3 / 9783031382833 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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