RADAR Sensing for Human Activity Monitoring of Daily Living Simultaneously in Multiple Subjects

雷达感测用于同时监测多个对象的日常生活活动

• 批准号: EP/W037076/1
• 负责人: Syed Aziz Shah
• 金额: $ 42.67万
• 依托单位: Coventry University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW037076%2F1
• 关键词: RADAR; Sensing; Human; Activity; Monitoring

As the COVID-19 disease began to spread across the world, the elderly population (aged 65+) experienced greater adverse effects from the pandemic, including more severe complications, higher mortality and disruptions to monitoring their human activities of daily living (ADL), including critical events (falls and wandering behavior) and access to care [1]. This disruption has been observed at homes and in care homes, where contact with family members and caregivers became more limited, due to isolation and lockdown. Statistics indicate that more than 70% of the elderly experience the types of critical events mentioned earlier when performing ADL, and their consequences can lead to a decreased quality of life and serious injuries, as well as heavy financial impact on the health and social care services. Innovative technological solutions, such as remote continuous monitoring using sensing devices, have the potential to improve quality of life and preserve safe, independent living with dignity, especially under isolation and lockdown, in homes or care homes. Sensing techniques including contact approaches (accelerometer, magnetometer and gyroscope) and non-contact approaches (acoustic-based, vision-based sensors, and pyroelectric infrared) have all been used at some stage in the monitoring of older adults' ADL. However, contact systems are often expensive, have sticky electrodes which can cause skin irritation during long-term monitoring and can generate noisy signals with poor contact. Non-contact systems, on the other hand, can be affected by light intensity in the space being monitored, low illumination conditions and evoke costly overheads in terms of complicated hardware installation, diverse maintenance needs and raise privacy concerns. RADAR systems use the reflected RF signal when encountering a moving subject within its range and work contactlessly in low illumination conditions, requiring no recording of images/videos, demanding no alterations to ADL and there is no element of stigmatizing the person because of their health problems and we believe are more likely to be accepted by users. The primary aim of this project is to develop and evaluate a multistatic (multiple RADAR sensor nodes) RADAR sensing system to monitor the ADL including but not limited to walking, sitting down, standing up, eating, lying on bed and picking up objects in multiple older adults simultaneously using machine learning algorithms. Specifically, the aim is to capture critical events such as falls and wandering behaviour.This multidisciplinary project will combine state-of-the-art research in the field of RADAR sensing technologies, advanced signal processing and machine learning, as well as engagement from potential end-users (older adults, family members, residential care staff).Reference[1]. Wu, B. Social isolation and loneliness among older adults in the context of COVID-19: a global challenge. glob health res policy, 2020.

随着COVID-19疾病开始遍及世界，老年人口（65岁以上）受到大流行的不利影响，包括更严重的并发症，更高的死亡率和中断，以监测其日常生活（ADL）的人类活动（ADL），包括关键事件（跌倒和流浪行为）和获得护理[1]。由于隔离和锁定，在家庭和护理院中观察到了这种破坏，在那里与家人和照顾者的接触变得更加有限。统计数据表明，超过70％的老年人经历了执行ADL时前面提到的关键事件的类型，其后果可能导致生活质量和严重伤害，以及对健康和社会护理服务的重大影响。创新的技术解决方案，例如使用传感设备进行远程连续监控，有可能改善生活质量，并在家庭或养老院中保存安全，独立生活，尤其是在隔离和锁定下。在某个阶段，在监测老年人ADL的某个阶段都使用了传感技术（加速度计，磁力计和陀螺仪）以及非接触式方法（基于声学的基于视觉的传感器和高电红色）。但是，接触系统通常很昂贵，具有粘性电极，可能会在长期监测过程中引起皮肤刺激，并且会产生嘈杂的信号，接触不良。另一方面，非接触系统可能会受到监控空间，低照明条件的光强度的影响，并在复杂的硬件安装，多样化的维护需求和提高隐私问题方面引起了昂贵的开销。雷达系统在其范围内遇到一个移动主题时使用反射的RF信号，并且在低照明条件下无接触地工作，不需要记录图像/视频，因此不需要对ADL进行更改，并且由于其健康问题而没有污名化的元素而且我们认为更有可能被用户接受。该项目的主要目的是开发和评估一个多静态（多个雷达传感器节点）雷达传感系统，以监视ADL，包括但不限于行走，坐下，站起来，站起来，吃饭，躺在床上躺在床上并在多个中捡起物体老年人同时使用机器学习算法。具体而言，目的是捕捉关键事件，例如瀑布和流浪行为。该多学科项目将在雷达传感技术，高级信号处理和机器学习以及潜在的参与方面结合最新的研究。最终用户（老年人，家庭成员，住宅护理人员）。参考[1]。 Wu，B。在Covid-19的背景下，老年人的社会隔离和孤独：全球挑战。 Glob Health Res政策，2020年。

项目成果

British Sign Language Detection Using Ultra-Wideband Radar Sensing and Residual Neural Network

• DOI: 10.1109/jsen.2024.3364389
• 发表时间: 2024-04-01
• 期刊: IEEE SENSORS JOURNAL
• 影响因子: 4.3
• 作者: Saeed，Umer;Shah，Syed Aziz;Abbasi，Qammer H.
• 通讯作者: Abbasi，Qammer H.