Machine Learning and Deep Learning in Human Activity Recognition and Fall Detection: Algorithms, Frameworks, and Applications for Sustainable Healthcare (Signals and Communication Technology) - Tapa dura

Acerca del autor

Dr. Suparna Biswas is an Associate Professor in the Department of Computer Science & Engineering in Maulana Abul Kalam Azad University of Technology, WB. She has received ME and Ph.D from Jadavpur University, West Bengal. She has been an ERASMUS MUNDUS Post Doctoral research fellow in Northumbria University, Newcastle, UK. She has successfully executed two funded research projects in the capacity of PI and Co-PI in the area of IoT based smart healthcare and security. She has been Lead Editor of an edited book entitled “Internet of Things based Smart healthcare” published by Springer in 2020. She has organized International Conference as Lead organizing chair, TPC member, acted as Session Chair, delivered Tutorial Lectures in International Conferences, and Invited talks in FDPs, Seminars etc. She is member of Board of Studies, Selection Committee member, PhD research Committee member etc. in different Universities. She has co-authored a number of research papers published in reputed journals including Q1 journals, conferences and book chapters of international repute. She has guided successfully 6 PhD scholars in the domain of smart healthcare and security. She has been awarded with two International patents for innovation in development of a Smart Health Monitoring Assistance prototype for Parkinson’s disease people. This innovation has been covered by National level reputed English news daily. Her areas of research interests are Internet of Things, Machine Learning, Network Security, Smart Healthcare, Smart Agriculture etc. She is a senior member of IEEE.

De la contraportada

This book presents research into the domain of Human Activity Recognition (HAR) and Fall Detection (FD), with a focus on the seamless monitoring and support of elderly people. The author shows how current HAR and FD technologies have application in disease monitoring, prediction and identification, as well real-time facilitating early diagnosis of symptom-based disease identification, prediction, and detection. The author discusses existing infrastructure that supports this ecosystem, comprising smartphones, WiFi, 3G/4G Internet connectivity, and low-cost wearable sensors for sustainable health monitoring and care. The book presents smart technologies such as machine learning, deep learning, and Internet of Things that are applied for sensor data analysis and knowledge extraction towards accurate identification of activities and fall events with pre-fall postures in real time. The author also shows how smart and seamless health monitoring and care ecosystem fits with traditional healthcare system for sustainable solutions.

• Presents smart technologies for sustainable health monitoring and care targeted for the elderly;
• Discusses techniques for privacy surrounding Human Activity Recognition (HAR) and Fall Detection (FD);
• Includes case studies, scenario-based studies, sponsored projects, prototypes and successful applications.