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Physical Sciences and Mathematics Commons

Open Access. Powered by Scholars. Published by Universities.®

Databases and Information Systems

Wright State University

Series

Activity Monitoring

Publication Year

Articles 1 - 2 of 2

Full-Text Articles in Physical Sciences and Mathematics

Evaluating A Potential Commercial Tool For Healthcare Application For People With Dementia, Tanvi Banerjee, Pramod Anantharam, William L. Romine, Larry Wayne Lawhorne Jul 2015

Evaluating A Potential Commercial Tool For Healthcare Application For People With Dementia, Tanvi Banerjee, Pramod Anantharam, William L. Romine, Larry Wayne Lawhorne

Kno.e.sis Publications

The widespread use of smartphones and sensors has made physiology, environment, and public health notifications amenable to continuous monitoring. Personalized digital health and patient empowerment can become a reality only if the complex multisensory and multimodal data is processed within the patient context, converting relevant medical knowledge into actionable information for better and timely decisions. We apply these principles in the healthcare domain of dementia. Specifically, in this study we validate one of our sensor platforms to ascertain whether it will be suitable for detecting physiological changes that may help us detect changes in people with dementia. This study shows …


Predicting Parkinson's Disease Progression With Smartphone Data, Pramod Anantharam, Krishnaprasad Thirunarayan, Vahid Taslimi, Amit P. Sheth Mar 2013

Predicting Parkinson's Disease Progression With Smartphone Data, Pramod Anantharam, Krishnaprasad Thirunarayan, Vahid Taslimi, Amit P. Sheth

Kno.e.sis Publications

Most of the existing approaches for detecting diseases/risk score form observations (sensor and textual) ignore the presence of any prior knowledge of the disease. In this work, we start top-down by enumerating the symptoms of Parkinson's Disease (PD) and map the symptoms to its possible manifestations in sensor observations (bottom-up). We show such manifestations and further use these manifestations as features to build classifiers to differentiate between the PD patients and the control group.