L2M NSERC - UWB Radar-Based Indoor Human Event Monitoring System

L2M NSERC - 基于 UWB 雷达的室内人体事件监测系统

• 批准号: 580751-2023
• 负责人: Zhu, WeiPingWP
• 金额: $ 1.46万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Idea to Innovation
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752005
• 关键词: L2M; NSERC; UWB; Radar; Based

Owing to the wide spreading of the highly contagious coronavirus (COVID-19), the whole world has suffered a shortage of healthcare resources in the last three years. However, the majority of infected patients are found to have light or moderate symptoms and thus are suggested to rest and recover at home. As reported by the Public Health Agency of Canada, 97.6% of COVID-19 deaths in Quebec occurred in the age group of 60 years and older. Moreover, a significant number of COVID-19 deaths at home have been reported in resting state (sleep) or fall down situations, caused mainly by a sudden deterioration of breathing along with other related symptoms. On the other hand, monitoring of human activities concerns several issues, including the feasibility and reliability of the technology used, the cost of sensing devices and the privacy of the subject. Currently, video monitoring and wearable sensor have been developed for smart sensing. Although straightforward, they have two limitations: privacy issues and the presence of blind spots to the camera. Meanwhile, wearable sensors are usually not user-friendly and are easily forgotten by elders. Developing an intelligent indoor surveillance system that can accurately detect abnormal events like falls as well as vital status in a way that protects people's privacy and allows easy access to data is a potential challenge. Motivated by the above observation, in this project, we will develop a non-contact indoor sensing system, exploiting the impulse radio ultra-wide-band (IR-UWB) radar and microphone array technology for human locations, behaviors (especially fall down and cough) and vital signs. The technical objectives of this project are to (1) augment existing sensing systems by developing and optimizing vital sign (breathing, heart rate, and heart sound) detection algorithms; (2) innovate recognition algorithms based on UWB radar and microphone array, which are capable of real-time identification of events and activities (e.g. walking, falling, eating, drinking, sleeping, coughing) as well as early prediction of disorders (such as Alzheimer's, Parkinson's, or COVID); and (3) develop a compact ambient sensor capable of detecting moving object through the cloud without calibration and risk of safety.

由于高传染性冠状病毒（COVID-19）的广泛传播，全球在过去三年遭遇医疗资源短缺。然而，大多数感染者被发现有轻微或中度症状，因此建议在家休息和恢复。根据加拿大公共卫生署的报告，魁北克97. 6%的COVID-19死亡发生在60岁及以上的年龄组。此外，据报道，大量COVID-19在家中死亡的病例是在休息状态（睡眠）或跌倒的情况下发生的，主要是由于呼吸沿着其他相关症状突然恶化造成的。另一方面，监测人类活动涉及若干问题，包括所用技术的可行性和可靠性、传感设备的成本以及受试者的隐私。目前，视频监控和可穿戴传感器已被开发用于智能传感。虽然简单，但它们有两个局限性：隐私问题和摄像头盲点的存在。与此同时，可穿戴传感器通常不方便用户使用，很容易被老年人遗忘。开发一种智能室内监控系统，可以准确检测福尔斯等异常事件以及重要状态，保护人们的隐私，并允许轻松访问数据，这是一个潜在的挑战。基于上述观察，在本项目中，我们将开发一种非接触式室内传感系统，利用脉冲无线电超宽带（IR-UWB）雷达和麦克风阵列技术来测量人体位置，行为（特别是跌倒和咳嗽）和生命体征。该项目的技术目标是（1）通过开发和优化生命体征来增强现有的传感系统（呼吸、心率和心音）检测算法;（2）基于UWB雷达和麦克风阵列的创新识别算法，能够实时识别事件和活动（例如行走、跌倒、进食、饮水、睡眠、咳嗽）以及疾病的早期预测（如阿尔茨海默氏症，帕金森氏症或COVID）;以及（3）开发一种能够在没有校准和安全风险的情况下检测穿过云的移动物体的紧凑的环境传感器。