Healthcare video surveillance: toward automated monitoring systems

加拿大正在经历人口的重大变化，人口老龄化速度非常快。因此，必须在医疗保健领域进行创新，以确定适当的方式，利用技术为老年人遇到的问题找到巧妙的解决办法。因此，本研究项目的目的是为老年人开发和调整自动视频监控方法，使他们能够保持自主性并享受更多的日常生活。该研究分为三个不同的子项目，都需要使用单个或多个摄像头进行自动视频监控。在第一个子项目中，我们建议检测老年人的意外福尔斯，这是独居老年人的最大风险因素之一。我们的方法是基于人体形状的变化和3D运动的身体一般，特别是头部的组合。在同样的背景下，我们打算开发一种方法来测量老年人在他/她的公寓中的步态，以评估可能的退化或变化，以防止福尔斯和/或诊断其他问题（移动性降低、平衡性降低、身体僵硬增加、步幅缩短、步速减慢、肢体运动减少等）。该方法使用一个简化的三维关节骨架模型来分析步态。最后一项研究包括一个用于监测药物摄入的计算机视觉系统，这是老年人问题的另一个主要来源。该系统的核心是面部、手和药丸（瓶）的定位和跟踪，然后是使用多层场景模型的活动识别。我们相信，这种视频系统将通过克服其许多局限性而有利地补充用于远程监护的其他类型的传感器。

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.