Engaging the Patient into the Virtual Care Loop: An E-Health Framework using Data and Knowledge-Driven Methods for Ambient Sensor-Based Health Activity Monitoring to Remotely Determine Patient's Health and Functional Status

在加拿大，人们认识到需要有一个提供家庭护理的医疗保健系统，特别是对老年人、慢性病患者和精神疾病患者。最近的covid-19大流行进一步强调了虚拟护理计划的必要性和实用性，这些计划可以在患者安全的家中为他们提供远程治疗咨询。我们的目的是为老年人、慢性和精神疾病患者提供不打扰和隐私的远程监控，帮助他们自我管理自己的健康，保持他们的自主权、生活方式和舒适，同时在家中过着健康的生活。本研究提出了一种新颖的基于电子健康的虚拟护理平台，该平台利用环境智能在家庭环境中远程监测患者的健康状况，提供辅助医疗保健。环境智能是指利用环境传感器收集个人的位置和运动数据，通过引导和提醒来帮助人们进行日常生活活动的智能环境。我们提出了一种基于活动识别（AR）概念的新型虚拟护理方法。监测目标个体的日常生活活动，以确定健康和功能。人们注意到，当健康受到影响时，进行日常生活活动的能力也会受到影响，因此，通过确定日常生活活动随时间的变化，我们将能够发现健康状况的下降，并及时提供辅助保健干预措施。这项研究将在遥感、知识和数据驱动的决策支持和电子卫生系统方面进行科学调查，以开发创新的不引人注目的AR方法，用于远程监测患者的日常活动。AR方法将用于生成数字生物标志物，以评估个人进行日常生活活动的能力。在遥感方面，我们将使用非接触式传感器收集患者的活动数据。我们将开发一种新的混合增强现实解决方案，其中知识驱动的方法将用于表示患者的家庭环境和日常生活活动模型，数据驱动的方法将用于识别患者的日常生活活动。基于识别的活动，我们将生成数字生物标志物来确定患者的健康状况。AR和健康评估方法将作为电子健康虚拟护理平台实施，供医疗保健提供者远程监测患者的健康状况。该项目的重要意义在于为虚拟护理提供电子卫生平台，使老年人和患有痴呆症、精神健康和慢性病的患者能够安全地生活在家中。鉴于加拿大老年人的增加以及长期护理机构中因COVID-19造成的生命损失，这项研究是及时和相关的。该项目将开发新的增强现实方法，以识别多人家庭中特定人员的活动，并随着时间的推移个性化活动模型。

In Canada, there is a recognition of the need to have a healthcare system which provides at--home care, especially to the elderly, chronic and mental health patients. The recent covid-19 pandemic further emphasized the need and utility of virtual care programs that offer remote therapeutic consultations to patients in the safety of their homes. Our intent is to provide unobtrusive and privacy-preserving remote monitoring of seniors, chronic and mental health patients' living at home, to assist them self-manage their health, maintain their autonomy, lifestyle and comfort whilst living a healthy life at their homes. This research proposes a novel e--Health based virtual care platform that employs ambient intelligence to provide assistive healthcare by remotely monitoring a patient's health in a home setting. Ambient intelligence refers to smart environments that use environmental sensors to collect location and movement data of individuals to help them perform daily-life activities by providing guidance and reminders. We are proposing a novel virtual care approach based on the concept of Activity Recognition (AR)-i.e. monitoring the targeted individual's daily life activities to determine health and function. It has been noted that when health is affected, the ability to carry out routine daily-life activities is also affected, so by identifying variations in daily-life activities over time we will be able to detect decline in health and deliver timely assistive healthcare interventions. This research will pursue scientific investigations in remote sensing, knowledge-- and data--driven decision support and e--Health systems to develop innovative unobtrusive AR methods for remote monitoring of a patient's daily activities. The AR methods will be used to generate digital biomarkers to assess the ability of an individual to perform daily-life activities. For remote sensing, we will use contactless sensors to collect the patients' activity data. We will develop a novel hybrid AR solution, where knowledge--driven methods will be used to represent the patient's home environment and daily-life activity models, and data--driven methods to recognize the patients' daily-life activities. Based on the recognized activities, we will generate digital biomarkers to determine the patient's health status. The AR and health assessment methods will be implemented as an e-Health virtual care platform for healthcare providers to remotely monitor their patient's health status. This project's significance is an e-Health platform for virtual care, enabling seniors and patients with dementia, mental health and chronic conditions to safely live in their homes. This research is timely and relevant given the increase in elderly Canadians and in light of the loss of life due to COVID-19 in long-term care facilities. The project will develop novel AR methods to identify the activities of a specific person in a multi-person home and personalize activity models over time.