Wearable and Wireless Systems for Healthcare I

Wearable and wireless systems for gait analysis and reflex quantification.- Traditional clinical evaluation of gait and reflex response by ordinal scale.- Quantification systems appropriate for a clinical setting.- The rise of inertial measurement units.- Portable wearable and wireless systems for gait and reflex response quantification.- Smartphones and portable media devices as wearable and wireless systems for gait and reflex response quantification.- Bluetooth inertial sensors for gait and reflex response quantification with perspectives regarding Cloud Computing and the Internet of Things.- Quantifying the spatial position representation of gait through sensor fusion.- Role of machine learning for gait and reflex response classification.- Homebound therapy with wearable and wireless systems.- Future perspective of Network Centric Therapy.

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.