Vision-Based Evaluation of Mobility, Physical Health, and Rehabilitation Progress in Natural Settings

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The high level objective of this program of research is to develop computer vision tools and algorithms capable of monitoring the movements and actions of humans in natural settings. The application area of this research is in the evaluation of mobility, physical health, and rehabilitation progress in the homes of older adults. Specifically, we will focus on observing the motion, pose, and daily activities of older adults in the home in order to automatically and continually assess their physical health and mobility. Advances in medical care and the aging of the baby boomer generation have resulted in the increasing size and proportion of the older adult population. Promoting and supporting aging-in- place alleviates some of the healthcare pressures resulting from this demographic change. Without proper considerations, however, living alone could pose serious health and safety risks for many older adults, particularly for those with chronic mobility or cognitive conditions. Earlier research has shown the acceptability of home video monitoring technologies among older adults if the technology allowed the participants to remain in their own homes. Recent advances in computer vision and machine learning have made real-time mobility and physical health monitoring in natural settings, i.e., in the home, a within reach possibility with high potentials in improving the quality of life of older adults, while at the same time posing interesting and challenging computer science research questions. Individuals or companies interested in learning more about this program of research or interested in contributing or collaborating towards its development are encouraged to contact the principal investigator (B. Taati) via email at taati@cs.utoronto.ca.