Wearable Telemedicine Technology for the Healthcare Industry Product Design and Development
Coordonnateurs : Gupta Deepak, Khanna Ashish, Hemanth D. Jude, Khamparia Aditya
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.
1. Human Body Interaction Driven Wearable Technology for Vital Signal Sensing 2. HealthWare Telemedicine Technology (HWTT) Evolution Map for Healthcare 3. Blockchain: A Novel Paradigm for Secured Data Conduct in Telemedicine 4. Wearable Technology and Artificial Intelligence in Psychiatric Disorders 5. Applying wearable smart sensors for vital signs controlling of patients in epidemics 6. A Novel Compressive Sensing with Deep Learning based Disease Diagnosis Model for Smart Wearable Healthcare Devices 7. Blockchain based Secure Data Sharing Scheme using Image Steganography and Encryption Techniques for Telemedicine Applications 8. Intelligent Metaheuristic Cluster based Wearable Devices for Healthcare Monitoring in Telemedicine Systems 9. Class Imbalance Data Handling with Deep Learning based Ubiquitous Healthcare Monitoring System using Wearable Devices 10. IoT and Wearables for Detection of COVID-19 Diagnosis using Fusion based Feature Extraction with Multi-Kernel Extreme Learning Machine 11. Internet of Things and Wearables Enabled Alzheimer detection and Classification Model using Stacked Sparse Autoencoder
Dr. Ashish Khanna has 16 years of expertise in teaching, entrepreneurship, and research and development. He received his PhD from the National Institute of Technology, Kurukshetra, India, and completed a post-doc degree at the National Institute of Telecommunications (Inatel), Brazil. He has published around 40 SCI-indexed papers in 'IEEE Transactions', and in other reputed journals by Springer, Elsevier, and Wiley, with a cumulative impact factor of above 100. He has published around 90 research articles in top SCI/Scopus journals, conferences, and book chapters. He is co-author or editor of numerous books, including 'Advanced Computational Techniques for Virtual Reality in Healthcare' (Springer), 'Intelligent Data Analysis: From Data Gathering to Data Comprehension' (Wiley), and 'Hybrid Computational Intelligence: Challenges and Applications' (Elsevier). His research interests include distributed systems, MANET, FANET, VANET, Internet of Things, and machine learning. He is
- 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
Date de parution : 11-2021
Ouvrage de 192 p.
19x23.4 cm
