E-Health Platform for Remote Health Monitoring using Automated Activity Recognition to Assist Self-Care and Healthy Lifestyle Behaviours

加拿大卫生系统中虚拟医疗项目的日益普及，扩大了对实用、安全和创新的电子卫生患者监测解决方案的需求。智能家居的出现，配备了传感器和物联网设备，提供了一种创新的电子健康解决方案，以监测和支持患者在家中进行日常生活自理活动（ADL）。活动识别（AR）是一种人工智能（AI）驱动的方法，可以使用低成本的传感器数据自主识别个人的活动；通过不使用视频监控，增强现实是一种保护隐私的患者监控方法。将智能家居与基于人工智能的增强现实相结合，为电子健康患者监测提供了一种新颖的方法，通过这种方法，我们可以监测患者的自我保健日常生活，并通过提供个性化指导和行为改变计划来帮助他们完成自我保健日常生活，从而灌输自给自足的能力，帮助他/她完成规定的自我保健日常生活。

The rising popularity of virtual care programs in the Canadian health system has amplified the demand for practical, safe and innovative eHealth patient monitoring solutions. The emergence of smart homes, outfitted with sensors and IoT devices, offer an innovative eHealth solution to monitor and support patients to perform self-care Activities of Daily Living (ADL) at their homes. Activity Recognition (AR) is an Artificial Intelligence (AI) driven approach to autonomously recognize the activities of an individual using low-cost sensor data; by not using video monitoring AR is a privacy-preserving method for patient monitoring. Combining smart homes with AI-based AR offers a novel approach to eHealth patient monitoring, whereby we can monitor an patient's self-care ADL, and help them complete the self-care ADL by providing personalized guidance and behaviour change programs to instil self-sufficiency to help him/her complete the prescribed self-care ADL. The long-term objective of the proposed research program is to investigate and develop AI-based data- and knowledge-driven AR methods to implement a privacy-preserving eHealth patient monitoring platform tp deliver virtual care to individuals with chronic diseases, dementia, disability and the elderly. The short-term objective is develop knowledge-driven AR solutions for eHealth based patient monitoring by novel semantic web based knowledge-driven AR methods that (a) improve the accuracy, robustness and flexibility of AR methods for recognizing self-care ADL within a smart home, and (b) demonstrate a prototype AR based eHealth patient monitoring platform that offers (i) personalized self-care ADL assistive acts; and (ii) behaviour change interventions to help patients perform self-care ADL. The societal objective is to improve the wellbeing of Canadians, whilst saving cost to the Canadian health system. This research is inspired by Artificial Intelligence (AI) in Medicine. T he research objectives will be pursued by developing Knowledge-driven AR methods to (a) Represent ADL processes using semantic models to achieve AR robustness and flexibility; (b) Represent an AAL environment using semantic models, to achieve AR accuracy and flexibility; and (c) Recognize self-care ADL, using semantic reasoning, whilst handling any uncertainty due to sensor noise and incorrect task sequences, to achieve AR accuracy and robustness. The research results will be translated as a prototype AR based eHealth patient monitoring platform. The research will impact eHealth based virtual care by demonstrating the technical feasibility of low-cost privacy-preserving AR methods for remote patient monitoring. The AR methods will be applicable for real-world applications, including astronaut monitoring, smart classrooms and security. The research will advance autonomous eHealth based virtual care applications, such as the use of personal care robots and IoT devices to provide timely assistance to vulnerable individuals.