Convolutional Neural Networks for the Real-Time Monitoring of Vital Signs Based on Impulse Radio Ultrawide-Band Radar during Sleep

被引:9
作者
Choi, Sang Ho [1 ]
Yoon, Heenam [2 ]
机构
[1] Kwangwoon Univ, Sch Comp & Informat Engn, Seoul 01897, South Korea
[2] Sangmyung Univ, Dept Human Ctr Artificial Intelligence, Seoul 03016, South Korea
基金
新加坡国家研究基金会;
关键词
IR-UWB radar; noncontact; vital-sign monitoring; real time; deep learning; UWB; GESTURES;
D O I
10.3390/s23063116
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Vital signs provide important biometric information for managing health and disease, and it is important to monitor them for a long time in a daily home environment. To this end, we developed and evaluated a deep learning framework that estimates the respiration rate (RR) and heart rate (HR) in real time from long-term data measured during sleep using a contactless impulse radio ultrawide-band (IR-UWB) radar. The clutter is removed from the measured radar signal, and the position of the subject is detected using the standard deviation of each radar signal channel. The 1D signal of the selected UWB channel index and the 2D signal applied with the continuous wavelet transform are entered as inputs into the convolutional neural-network-based model that then estimates RR and HR. From 30 recordings measured during night-time sleep, 10 were used for training, 5 for validation, and 15 for testing. The average mean absolute errors for RR and HR were 2.67 and 4.78, respectively. The performance of the proposed model was confirmed for long-term data, including static and dynamic conditions, and it is expected to be used for health management through vital-sign monitoring in the home environment.
引用
收藏
页数:15
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