Person-Specific Heart Rate Estimation With Ultra-Wideband Radar Using Convolutional Neural Networks

Shuqiong Wu,Toru Sato,Takeshi Fukuda,Kenichi Inoue,Kenji Mizutani,Takuya Sakamoto,Kentaro Oishi,Hiroyuki Sakai

doi:10.1109/access.2019.2954294

Abstract

Vital-sign estimation using ultra-wideband (UWB) radar is preferable because it is contactless and less privacy-invasive. Recently, many approaches have been proposed for estimating heart rate from UWB radar data. However, their performance is still not reliable enough for practical applications. To improve the accuracy, this study employs convolutional neural networks to learn the special patterns of the heartbeats. In the proposed system, skin displacements of the target person are measured using UWB radar, and the radar signal is converted to a two-dimensional matrix, which is used as the input of the designed neural networks. Meanwhile, two triangular waves corresponding to the peaks and valleys in an electrocardiogram are adopted as the output of the networks. The proposed system then identifies each individual and estimates the heart rate automatically based on the already trained neural networks. The estimation error of the interbeat interval computed using our approach was reduced to 4.5 ms in the best case; and 48.5 ms in the worst case. Experiment results show that the proposed approach significantly outperforms a conventional method. The proposed machine learning approach achieves both personal identification and heart rate estimation simultaneously using UWB radar data for the first time. Moreover, this study found that using the respiration and heartbeat components together may enhance the accuracy of heart rate estimation, which is counter-intuitive, because the respiration is usually believed to interfere with the heartbeat.

Highlights

In recent decades, computer-based vital signs monitoring has played an increasing role in the medical care and nursing fields
The radar echo from a range that corresponds to the left shoulder is extracted and used for personal identification and heartbeat monitoring in this study
Because the purpose of this study is to show for the first time the effectiveness of machine-learning-based heartbeat monitoring and personal identification using UWB radar, tackling this challenging problem is out of the scope of the present paper

Summary

Introduction

Computer-based vital signs monitoring has played an increasing role in the medical care and nursing fields. In hospitals, child care centers, and nursing homes, heart rate monitoring can avoid many accidents caused by cardiovascular diseases. Considering that multiple persons commonly share the same space, an ideal monitoring system would provide accurate vital data, and a function to identify each individual. Sensors worn on the wrist [4], [5] or in the ear [6] have been designed to monitor vital signs. Wearing these devices is troublesome; and may cause skin discomfort [7].

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