Wearable and Wireless Systems for Healthcare I (and Further) Edition(s): Gait and Reflex Response Quantification

This book is the second edition of the one originally published in 2017. The original publication features the discovery of numerous novel applications for the use of smartphones and portable media devices for the quantification of gait, reflex response, and an assortment of other concepts that constitute first-in-the-world applications for these devices. Since the first edition, numerous evolutions involving the domain of wearable and wireless systems for healthcare have transpired warranting the publication of the second edition.

This volume covers wearable and wireless systems for healthcare that are far more oriented to the unique requirements of the biomedical domain. The paradigm-shifting new wearables have been successfully applied to gait analysis, homebound therapy, and quantifiable exercise. Additionally, the confluence of wearable and wireless systems for healthcare with deep learning and neuromorphic applications for classification is addressed. The authors expect that these significant developments make this book valuable for all readers.

Dr. Robert LeMoyne presently is an Adjunct Professor for Northern Arizona University for the Department of Biological Sciences and an Adjunct Professor for Czech Technical University for the Department of Biomedical Technology. Dr. LeMoyne is researching advanced concepts for wearable and wireless systems, such as for the domain of gait, reflex response, movement disorders, and therapeutic interventions. His Ph.D. in Biomedical Engineering was earned at University of California Los Angeles (UCLA) (2010). He has first authored more than 150 technical proceedings, including four books, which have been cited more than 2000 times, spanning a broad array of subjects, such as the rampant assortment of applications for wearable and wireless systems.

Timothy Mastroianni is a Cognitive Scientist, Researcher, Entrepreneur. He is first to develop and use computer vision and pattern recognition in a non-invasive manner to discover the internal states of the random number generator in machines (HiLoClient). Later, he presented these algorithms and methods to Carnegie Mellon University to map the human brain using machine learning and fMRI to discover brain states during specific tasks.