Longitudinal at home mobility monitoring for aging-in-place applications

This project will address challenges associated with aging in place, particularly for older adults with mobility problems or neurodegenerative diseases. Dr. Qiyin Fang’s group has recently developed a multi-sensor system that is capable of monitoring physiological data and movement datalike that from a fitbit and integrating this with indoor positioning information about where a person is in their home. The system is composed of a wearable smart watch with custom software for data collection from the accelerometer, gyroscope and heartrate PPG sensors, as well as a series of several beacons installed in the house that will transmit location data. This indoor position data comes from a smart-watch and multiple ambient beacons within the house, that communicate with the watch, to triangulate the wearer’s functional location. Using this data, it should be possible to determine the activity types of the users by associating different physiological data with location. This would enable us to determine things like the length or frequency of activity, and the time spent being mobile versus time spend sedentary. However, currently, there do not exist algorithms to process and analyze this data. Once implemented, these indicators can be extrapolated to determine activities of daily living, and patterns of activities of daily living. The purpose of this project is to develop algorithms to process and interpret this data automatically, for these purposes