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

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. INDEX TERMS Ultra-wideband radar, heart rate, vital signs, convolutional neural networks.

Is the PPG Signal Chaotic?

“…Here we propose an encoder-fully connected residual architecture whose residual blocks are based in the ones from [17]. The encoder-fully connected architectures are traditionally used in classification tasks like [18][19][20][21][22][23], actually it is used in a range of applications like Wi-Fi people detection [24], regression [25][26][27][28] or indoor localization [29,30]. We define the proposed CNN as ours for three main reasons: first, that is a CNN explicitly created for 1D signal processing; at next, we prepare an structure capable of process high and low frequencies with the use of long kernels; finally, we base our residual blocks in ResNet [17], but all of them have been adapted to 1D processing.…”

Harmonic Multiple Loop Detection (HMLD) Algorithm for Not-Contact Vital Sign Monitoring Based on Ultra-Wideband (UWB) Radar

“…e −j2π ντ e −j2πft dtdτ (6) According to the calculation rule of double integral, it can be seen that the simplification result is as follows:…”

“…By measuring respiratory rate (RR) and heart rate (HR), medical staff can diagnose clinical disease for patients and monitor disease through vital signs [1]- [5]. In the health monitor of infants and the elderly, monitoring vital signs can observe physical changes and sleep quality, which plays a role in preventing sudden disease [6], [7]. The current common method of detecting vital signs is to contact the human body to obtain vital signs information, such as pulse-oximetry and electrocardiogram (ECG).…”

Contactless Real-Time Heartbeat Detection via 24 GHz Continuous-Wave Doppler Radar Using Artificial Neural Networks

“…The most frequent radar architectures used in heart rate estimation sensors are continuous-wave (CW) Doppler radars [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24], frequency-modulated continuous-wave (FMCW) radars [3,25], and impulse radio ultra-wideband (IR UWB) radars [26][27][28][29][30]. CW Doppler and FMCW radars mostly outperform IR UWB radars in terms of power consumption and sensitivity [2].…”

“…Recently, new approaches based on supervised or unsupervised machine-learning algorithms [31,32] were introduced in the CW Doppler radar systems, and the first results have shown promising advantages in terms of heartbeat detection delay and source separation capabilities (robustness of heartbeat detection to respiration motion or random body motion) compared to traditional approaches. Convolutional neural networks (CNN) were applied in [29] to estimate heart rate from UWB radar signals. However, due to the lack of training data, this approach was person-specific since the CNN needed to be trained for each subject separately.…”