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，全世界在过去三年中遭受了医疗资源的短缺。但是，发现大多数受感染的患者具有轻度或中度的症状，因此建议在家中休息和康复。正如加拿大公共卫生局报道的那样，魁北克的197.6％的魁北克死亡人数发生在60岁及以上的年龄段。此外，据报道，在静息状态（睡眠）或跌落情况下，大量的共同死亡在家中发生了大量的死亡，这主要是由于呼吸突然恶化以及其他相关症状的突然恶化。另一方面，对人类活动的监视涉及几个问题，包括所使用的技术的可行性和可靠性，感应设备的成本和受试者的隐私。当前，已经开发了视频监控和可穿戴传感器，以供智能传感。尽管很简单，但它们有两个局限性：隐私问题和相机上存在盲点。同时，可穿戴传感器通常不易于用户友好，长者很容易忘记。开发一个智能的室内监视系统，该系统可以准确地检测出异常事件，例如跌倒以及至关重要的状态，以保护人们的隐私并允许轻松访问数据是一个潜在的挑战。在该项目中，我们将开发一个非接触室内室内传感系统，利用脉冲无线电超宽波段（IR-UWB）雷达（IR-UWB）雷达和麦克风阵列技术，用于人类位置，行为（尤其是跌倒和咳嗽）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（呼吸，心率和心脏声音）检测算法来增强现有传感系统； （2）基于UWB雷达和麦克风阵列的创新识别算法，这些算法能够实时识别事件和活动（例如步行，跌倒，跌倒，进食，饮酒，睡眠，咳嗽）以及早期预测疾病（例如阿尔茨海默氏症，帕金森氏症或covid）； （3）开发一个紧凑的环境传感器，能够通过云通过云检测移动物体而无需校准和安全风险。