Predictive health by inferring the health condition of individuals through the analysis of their activity patterns at home
This interdisciplinary collaboration between Emory’s Center for Comprehensive Informatics and Georgia Tech’s School of Interactive Computing investigates new approaches for Infrastructure-mediated sensing (IMS) as an approach to human activity recognition, based on the idea that human activities (e.g. running a dishwasher, turning on a reading light, or walking through a doorway) can be sensed via their manifestations in an environment’s existing infrastructures (e.g. a home’s water, electrical, and HVAC infrastructures). Because of its practical, low-cost, and unobtrusive nature, infrastructure-mediated sensing offers significant promise as a general method for activity recognition. This initiative proposes to apply, e.g., water-based IMS to infer medically-meaningful activities of individuals who suffer from HIV/Cancer. We believe that by accurately tracking and logging human behavior in this context, and integrating the behavioral data into patient's clinical profile, physicians will have access to an additional layer of information from which to prescribe medication, examine behavior, modify recovery programs, etc. We leverage knowledge and facilities from physicians from the Emory medical community, and determine types of activities of daily living (ADL) information which would lead to improved decision support for physicians caring for patients, and deploy our sensor in patients home and integrate all the acquired data into the grid computing/database framework.