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<p>Chapter.1.Introduction.- Chapter.2.Contactless Activity Identification Using Commodity WiFi.- Chapter.3.Personalized Fitness Assistance using Commodity WiFi.- Chapter.4. Multi-person Fitness Assistance via Millimeter Wave.- Chapter.5.Non-intrusive Eating Habits Monitoring Using Millimeter Wave.- Chapter.6.Fitness Assistance Using Motion Sensor.- Chapter.7.Fine-grained Gesture Recognition and Sign Language Interpretation via Photoplethysmography (PPG) on Smartwatches.- Chapter.8.Continuous User Authentication via PPG.- Chapter.9.Conclusion and Future Directions.</p>

<p>Xiaonan Guo received the Ph.D. degree in computer science and engineering from The Hong Kong University of Science and Technology, Hong Kong, in 2013. He is currently an Assistant Professor with the department of information science and technology at George Mason University. Prior that he was an Assistant Professor at Indiana University-Purdue University, Indianapolis. He was a Research Associate with the Department of Electrical and Computer Engineering, Stevens Institute of Technology, His research interests include pervasive computing, mobile computing, and cybersecurity and privacy. He has received the Best Paper Award from the ACM Conference on Information, Computer, and Communications Security (ASIACCS) in 2016 and the EAI International Conference on IoT Technologies for HealthCare (EAI Healthy IoT) in 2019.</p>

<p>Yan Wang is an Associate Professor in Computer &amp; Information Sciences Department at Temple University. Before that, he was with the Department of Computer Science at SUNY, Binghamton. He received his Ph.D. degree in Electrical Engineering from Stevens Institute of Technology. His research interests include Cyber Security and Privacy, Internet of Things, Mobile and Pervasive Computing, and Smart Healthcare. His research is supported by the National Science Foundation (NSF). He is the recipient of the NSF CAREER Award. He is the recipient of the Best Paper Award from IEEE CNS 2018, IEEE SECON 2017, and ACM AsiaCCS 2016. He is serving and has served on the organizing committee of ACM MobiCom, IEEE INFOCOM, ACM WiSec, IEEE MASS, IEEE DYSPAN, and IEEE CNS. He is the Associate Editor of IEEE Transactions on Information Forensics and Security and the guest editor of the special issue of the Journal of Surveillance, Security and Safety. He regularly serves on the technical program committees of Top-ranked ACM and IEEE conferences, including ACM MobiCom, ACM MobiSys, IEEE INFOCOM, IEEE ICDCS, IEEE CNS, IEEE ICC. He also serves as the reviewer for prestigious journals, including IEEE/ACM Transactions on Networking (IEEE/ACM ToN), IEEE Transactions on Mobile Computing (IEEE TMC), IEEE Transactions on Wireless Communications (IEEE TWireless), and EURASIP Journal on Information Security.</p>

<p>Jerry Cheng was an Assistant Professor with the Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, USA. He was formerly a Postdoctoral Researcher with the Department of Statistics, Columbia University, New York, NY, USA. He had extensive industrial experiences as a Member of Technical Staff at AT&amp;T Labs, Murray Hill, NJ, USA. He is currently an Assistant Professor of computer science with the New York Institute of Technology, New York. His background is a combination of computer science, statistics, and physics. His work has been reported by many new media, including MIT Technology Review, Yahoo News, Digital World, FierceHealthcare, and WTOP Radio. His research interests include big data analytics, statistical learning, Bayesian statistics, and their applications in computer systems and smart healthcare.</p>

<p>Yingying (Jennifer) Chen is a Professor and Department Chair of Electrical and Computer Engineering (ECE) and Peter Cherasia Endowed Faculty Scholar at Rutgers University. She is the Associate Director of Wireless Information Network Laboratory (WINLAB). She also leads the Data Analysis and Information Security (DAISY) Lab. She is a Fellow of Association for Computing Machinery (ACM), a Fellow of Institute of Electrical and Electronics Engineers (IEEE) and a Fellow of National Academy of Inventors (NAI). Her research interests include Applied Machine Learning in Mobile Computing and Sensing, Internet of Things (IoT), Security in AI/ML Systems, Smart Healthcare, and Deep Learning on Mobile Systems. She is a pioneer in RF/WiFi sensing, location systems, and mobile security. Before joining Rutgers, she was a tenured professor at Stevens Institute of Technology and had extensive industry experiences at Nokia (previously Lucent Technologies). She has published 3 books, 4 book chapters and 300+ journal articles and refereed conference papers. She is the recipient of seven Best Paper Awards in top ACM and IEEE conferences. She is the recipient of NSF CAREER Award and Google Faculty Research Award. She received NJ Inventors Hall of Fame Innovator Award and is also the recipient of IEEE Region 1 Technological Innovation in Academic Award. Her research has been supported by many funding agencies including NSF, NIH, ARO, DoD and AFRL and reported in numerous media outlets including MIT Technology Review, CNN, Wall Street Journal, National Public Radio and IEEE Spectrum. She has been serving/served on the editorial boards of IEEE Transactions on Mobile Computing (TMC), IEEE Transactions on Wireless Communications (TWireless), IEEE/ACM Transactions on Networking (ToN) and ACM Transactions on Privacy and Security.</p>

<p>This book offers a comprehensive examination of mobile technologies in healthcare. It starts by covering wireless solutions, including WiFi signals and mmWave technology for activity recognition, fitness assistance, and eating habit monitoring. The discussion extends to wearable technologies that focus on personal fitness and injury prevention, highlighting the innovative use of PPG sensors in wearables, which enable gesture recognition and user authentication.</p>

<p>Based on thorough analyses on the challenges of designing robust mobile healthcare systems, this book addresses the difficulty of gathering accurate and reliable sensor data amidst the variability of human activities. It explores solutions using advanced sensing modalities, such as WiFi, mmWave, and PPG sensors, and robust algorithms for feature extraction to interpret activities, gestures, and biometrics. It also tackles system robustness across diverse environments and practical issues such as reducing training efforts, handling motion artifacts, and the implementation of these systems using commercially available devices.</p>

<p>The primary audience for this book targets computer science students and researchers working in mobile computing, smart healthcare, human-computer interaction and artificial intelligence/machine learning.&nbsp; Professionals and consultants focused on advancing mobile-based healthcare solutions will want to purchase this book as a reference.&nbsp;</p>

Presents edge mobile technologies to address key challenges in developing real-world healthcare applications Offers reducing training effort, handling motion artifacts and implementing systems using commercially available devices Focuses on practical AI/ML solutions