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

使用自动活动识别进行远程健康监测的电子健康平台，以协助自我护理和健康的生活方式行为

• 批准号: RGPIN-2020-05962
• 负责人: Abidi, Samina
• 金额: $ 2.55万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=740943
• 关键词: Health; Platform; Remote; Monitoring; using

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.

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、安全和创新的电子健康患者监测解决方案的需求。配备传感器和物联网设备的智能家居的出现，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自我护理日常生活活动（ADL）。活动识别（AR）是一种人工智能（AI）驱动的方法，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，AR是一种用于患者监控的隐私保护方法。将智能家居与基于AI的AR相结合，为电子健康患者监测提供了一种新的方法，我们可以通过提供个性化的指导和行为改变计划来监测患者的自我护理ADL，并帮助他们完成自我护理ADL，以帮助他/她完成规定的自我护理ADL。 拟议研究计划的长期目标是研究和开发基于人工智能的数据和知识驱动的增强现实方法，以实现隐私保护的电子健康患者监测平台，为慢性病患者，痴呆症患者，残疾人和老年人提供虚拟护理。短期目标是通过新颖的基于语义网的知识驱动的AR方法开发用于基于eHealth的患者监测的知识驱动的AR解决方案，（a）提高用于识别智能家居内的自我护理ADL的AR方法的准确性、鲁棒性和灵活性，以及（B）展示基于AR的eHealth患者监测平台原型，其提供（i）个性化的自我护理ADL辅助行为;及（ii）行为改变干预，以帮助患者进行自我护理ADL。社会目标是改善加拿大人的福祉，同时节省加拿大卫生系统的成本。这项研究的灵感来自于医学中的人工智能（AI）。研究目标是通过开发知识驱动的AR方法来实现：（a）使用语义模型表示ADL过程，以实现AR的鲁棒性和灵活性;（B）使用语义模型表示AAL环境，以实现AR的准确性和灵活性;以及（c）使用语义推理来识别自我护理ADL，同时处理由于传感器噪声和不正确的任务序列而引起的任何不确定性，以实现AR准确性和鲁棒性。研究结果将转化为基于AR的eHealth患者监测平台原型。 该研究将通过展示用于远程患者监测的低成本隐私保护AR方法的技术可行性来影响基于电子健康的虚拟医疗。AR方法将适用于现实世界的应用，包括宇航员监控、智能教室和安全。该研究将推进基于自主电子健康的虚拟护理应用，例如使用个人护理机器人和物联网设备为弱势群体提供及时帮助。