Responsive Home - Integrating Machine Learning with Home-based Sensors to Provide Incident Response for Seniors Living Independently

与许多其他工业化国家一样，加拿大面临着适应迅速老龄化的人口结构的挑战。我们的国家需要创新和变革性的解决方案，以使我们的医疗保健和社会系统在老年人口不断增长的情况下保持强大和可持续。能够“就地养老”并保持较长时间的独立生活是这一背景下的一个重要目标。大多数加拿大人喜欢就地养老和独立生活，而不是其他形式的护理(例如，住宿护理)，如果他们感到得到足够的支持和照顾的话。预计信息和通信技术(信通技术)将在实现这一愿景方面发挥重要作用。老年人的家庭环境配备了智能传感器，可能能够预防或检测与健康和安全相关的事件，如跌倒或疾病的发作。由于生活方式和生活环境的多样性，这样的“智能家居”技术必须适应个人的特定习惯和需求。机器学习算法可以用于基于来自各种基于家庭的传感器的活动数据流来提供这种智能适应。我们的公司合作伙伴(Tochtech)是一家医疗保健技术提供商初创公司，正在开发asmart Home Package，以支持照顾独立生活的老年人。Tochtech的解决方案将能够监控老年人的日常活动数据，并在检测到潜在的安全或健康风险时发出警报。Tochtech有兴趣探索机器学习算法、平台和工具的使用和评估，以提供一个自适应的事件检测系统。他们已经与维多利亚大学的领先实验室合作，提供专业知识和指导。NSERC Engage项目的目标是(1)评估当前的机器学习算法、平台和工具，并选择最适合应用问题的算法；(2)构建用于检测两个选定风险类别(意外跌倒和急性疾病)的机器学习系统的原型；以及(3)设计一个可扩展的体系结构平台，用于添加检测算法以应对其他风险类别。

Like many other industrialized nations, Canada faces the challenge of adapting to a rapidly aging demographic.Our country requires innovative and transformative solutions in order for our health care and social systems toremain strong and sustainable, in presence of a growing population of senior citizens. The ability to "age inplace" and remain living independently for a longer period of time is an important objective in this context.Most Canadians prefer aging in place and independent living over other forms of care (e.g., residential care), ifthey feel adequately supported and cared for. Information and communication technologies (ICT) are expectedto play a major role in enabling this vision. A senior's home environment equipped with smart sensors may beable to prevent or detect health and safety-related incidents, such as falls or the onset of an illness.Since there is a large diversity of lifestyles and living environments, such "Smart Home" technologies mustbe adaptive to the particular habits and needs of an individual. Machine learning algorithms can be used toprovide such intelligent adaptation based on activity data streams sourced from a variety of home-basedsensors. Our company partner (Tochtech) is a healthcare technology provider start-up firm who is developing aSmart Home package in support of caring for independently living seniors. Tochtech's solution will be capableof monitoring daily activity data of seniors and raising alerts when detecting potential safety or health risks.Tochtech is interested in exploring the use and evaluation of machine-learning algorithms, platforms and toolsfor the purpose of providing an adaptive incident detection system. They have engaged with the University ofVictoria's LEAD lab to provide expertise and guidance. The objectives of this NSERC Engage project are (1)to evaluate current machine learning algorithms, platforms and tools and select the ones most suitable for theapplication problem, (2) to prototype a machine learning system for detecting two selected risk categories(accidental falls and acute illness), and (3) to design an extensible architecture platform for adding detectionalgorithms to address additional risk categories.