A Contactless Health Monitoring System for Vital Signs Monitoring, Human Activity Recognition and Tracking

被引:1
|
作者
Li A. [1 ]
Bodanese E. [2 ]
Poslad S. [2 ]
Chen P. [3 ]
Wang J. [3 ]
Fan Y. [2 ]
Hou T. [4 ]
机构
[1] School of Computing and Communications, Lancaster University, Lancaster
[2] School of Electronic Engineering and Computer Science, Queen Mary University of London, London
[3] Electronic Information Engineering, Beihang University, Beijing
[4] School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing
基金
中国国家自然科学基金;
关键词
Deep learning; Human factors; Integrated sensing and communication; Monitoring; Privacy; Radar; Radar tracking; Remote health monitoring; Sensors; Social factors;
D O I
10.1109/JIOT.2023.3336232
中图分类号
学科分类号
摘要
Integrated sensing and communication technologies provide essential sensing capabilities that address pressing challenges in remote health monitoring systems. However, most of today’s systems remain obtrusive, requiring users to wear devices, interfering with people’s daily activities, and often raising privacy concerns. Herein, we present HealthDAR, a low-cost, contactless, and easy-to-deploy health monitoring system. Specifically, HealthDAR encompasses three interventions: i) Symptom Early Detection (monitoring of vital signs and cough detection), ii) Tracking & Social Distancing, and iii) Preventive Measures (monitoring of daily activities such as face-touching and hand-washing). HealthDAR has three key components: (1) A low-cost, low-energy, and compact integrated radar system, (2) A simultaneous signal processing combined deep learning (SSPDL) network for cough detection, and (3) A deep learning method for the classification of daily activities. Through performance tests involving multiple subjects across uncontrolled environments, we demonstrate HealthDAR’s practical utility for health monitoring. IEEE
引用
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页码:1 / 1
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