A UWB Radar-based Approach of Detecting Vital Signals

Qimeng Li, Jikui Liu, Raffaele Gravina, Ye Li, G. Fortino
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引用次数: 2

Abstract

The recent widespread pandemic of COVID-19 has put tremendous pressure on the healthcare system. The deployment of telehealth technology is crucial in solving this problem when patients are mildly ill and need to self-isolate at home or in a specific location. This paper proposes using a single radar sensor to continuously contact-less monitor the patients' vital signals in their daily lives. We use edge computing to handle high-priory tasks and combined cloud infrastructure for further process and storage to provide monitoring and telehealth services. A case study is presented to show how the approach can continuously monitor and recognize high-risk diseases and abnormal activity (e.g., sleep apnea). While an accident occurs, the system could provide fast and accurate emergency services. The work has been compared with a good standard. And the experimental results show that the proposed approach for heart rate (HR) and respiratory rate (RR) detection achieved a Mean Absolute Error (MAE) ± Standard Deviation of Absolute Error (SDAE) of 0.09±1.43 bpm and 0.23±3.23 bpm, respectively. This indicates the radar sensor can provide a high recognition accuracy to meet the requirements for a range of cardiopulmonary function monitoring. This kind of telemedicine service facilitates monitoring the self-isolated subjects to detect and recognize human physical and physiological activities.
一种基于超宽带雷达的生命信号检测方法
最近的COVID-19大流行给医疗保健系统带来了巨大压力。当患者病情轻微,需要在家中或特定地点进行自我隔离时,远程保健技术的部署对于解决这一问题至关重要。本文提出利用单个雷达传感器对患者日常生活中的生命信号进行连续无接触监测。我们使用边缘计算来处理高优先级任务,并结合云基础设施来进一步处理和存储,以提供监测和远程医疗服务。一个案例研究展示了该方法如何持续监测和识别高危疾病和异常活动(如睡眠呼吸暂停)。当事故发生时,该系统可以提供快速准确的应急服务。这项工作已被比较为合格。实验结果表明,该方法检测心率(HR)和呼吸频率(RR)的平均绝对误差(MAE)±绝对误差标准偏差(SDAE)分别为0.09±1.43 bpm和0.23±3.23 bpm。这表明雷达传感器可以提供较高的识别精度,以满足各种心肺功能监测的要求。这种远程医疗服务便于对自我隔离的受试者进行监测,以检测和识别人体的身体和生理活动。
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