Automated Monitoring and Localization of People

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The long-term objective of the proposed research program is to develop a system for comprehensive detection of activities as well as physiological assessment of people who need assistance in their natural environments. For example, this includes tracking the daily activities of elderly people, people with dementia, children, inmates, as well as detecting falls of elderly people and sudden life-threatening events such as heart attack. This project will allow for continuous monitoring of a person who lives alone or with other people, or for simultaneous monitoring of several people. The approach taken in this NSERC Discovery research program differs from research based on smart home and recognition of daily activities. Here, a complete solution for tracking and assessment of physiological states of multiple people in realistic environments based only on radio-frequency backscattering signals is proposed. We propose using a radar system for remote contactless localization of people, classification of their activities as well as estimation of their breathing rates. The radar system will be augmented with newly developed passive radio frequency identification (RFID) system that will assist in localization and distinguishing among multiple people. In cases when a single person is tracked and monitored, radar-only non-contact-based solution is sufficient. This research will place a strong emphasis on the development of new algorithms for localization, breathing rate estimation, classification and learning about the activities of people based on signals from radar and RFID systems. New hardware and algorithms for the RFID reader for determining direction of arrivals will be developed. The algorithms will be robust and designed to operate in realistic environments. Five graduate students and two post-doctoral fellows will be trained in this 5-year program. The project is important for Canada because it will result in improving quality of life of the people of Canada and abroad. In addition, it has a potential to result in significant savings in health-care costs because it will allow medical workers to detect events in progress, such as falls of elderly people, and to react on time.