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.

由于高度传染性的冠状病毒（COVID-19）的广泛传播，过去三年来，全世界都面临医疗资源短缺的问题。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康复。据加拿大公共卫生局报告，魁北克省 97.6% 的 COVID-19 死亡病例发生在 60 岁及以上的年龄组。此外，据报道，大量 COVID-19 在家中死亡是在静息状态（睡眠）或跌倒情况下发生的，这主要是由呼吸突然恶化以及其他相关症状引起的。另一方面，对人类活动的监测涉及多个问题，包括所用技术的可行性和可靠性、传感设备的成本以及主体的隐私。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：隐私问题和相机盲点的存在。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，能够准确检测跌倒等异常事件以及生命状态，同时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。受上述观察的启发，在这个项目中，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检测人体位置、行为（特别是跌倒和摔倒）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（呼吸、心率和心音）检测算法来增强现有传感系统； （2）创新基于UWB雷达和麦克风阵列的识别算法，能够实时识别事件和活动（例如行走、跌倒、吃、喝、睡觉、咳嗽）以及早期预测疾病（例如阿尔茨海默病、帕金森病或新冠肺炎）； (3) 开发一种紧凑型环境传感器，能够通过云检测移动物体，无需校准且存在安全风险。