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Wearable and Wireless Systems for Healthcare I Gait and Reflex Response Quantification von LeMoyne, Robert (eBook)

  • Erscheinungsdatum: 20.11.2017
  • Verlag: Springer-Verlag
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Wearable and Wireless Systems for Healthcare I

This book provides visionary perspective and interpretation regarding the role of wearable and wireless systems for the domain of gait and reflex response quantification. These observations are brought together in their application to smartphones and other portable media devices to quantify gait and reflex response in the context of machine learning for diagnostic classification and integration with the Internet of things and cloud computing. The perspective of this book is from the first-in-the-world application of these devices, as in smartphones, for quantifying gait and reflex response, to the current state of the art. Dr. LeMoyne has published multiple groundbreaking applications using smartphones and portable media devices to quantify gait and reflex response. Dr. Robert LeMoyne is currently serving as an Adjunct Professor of Biology, Department of Biological Sciences and Center for Bioengineering Innovation for Northern Arizona University. At Northern Arizona University he is researching advanced technology for wearable and wireless systems for biomedical applications. He earned his PhD in Biomedical Engineering from University of California Los Angeles (UCLA) during 2010. From 2010 to 2012 he served Sandia National Laboratories, and since 2013 he has been serving Northern Arizona University. From a biomedical engineering perspective his research interests emphasize prosthetic technologies, machine learning applications, and wearable and wireless systems for biomedical applications, such as through smartphones and portable media devices, for accessing health status. 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.


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