Healthcare video surveillance: toward automated monitoring systems

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Canada is experiencing significant changes in its population, which is aging very quickly. It is therefore essential to innovate in the area of healthcare to identify appropriate ways that technologies can be used to find ingenious solutions to the problems experienced by the elderly. The purpose of this research project is therefore to develop and adapt automatic video surveillance methods for elderly so that they can preserve their autonomy and enjoy more their daily lives. The research is divided into three different subprojects all requiring automatic video surveillance with single or many cameras. In the first subproject, we propose to detect accidental falls of elderly people, which are one of the greatest risk factor for seniors living alone. Our approach is based on a combination of human shape variation and 3D motion of the body in general and the head in particular. In the same context we intend to develop a method to measure the gait of the elderly in his/her apartment for assessing possible degradation or changes to prevent falls and/or diagnose other problems (reduced mobility, decreased balance, increased body stiffness, shorter step, slower pace, reduced motion of the limbs etc.). The method uses a simplified 3D articulated skeleton model to analyze the gait. The last study consists in a computer vision system for monitoring medication intake, another major source of problems for elderly persons. At the core of this system are face, hands and pills (bottles) localization and tracking followed by activity recognition with multi-level scenario models. We believe that such video systems will advantageously complement other types of sensors used for telemonitoring by overcoming many of their limitations.