Contactless Monitoring of PPG Using Radar

Abstract

In this paper, we propose VitaNet, a radio frequency based contactless approach that accurately estimates the PPG signal using radar for stationary participants. The main insight behind VitaNet is that the changes in the blood volume that manifest in the PPG waveform are correlated to the physical movements of the heart, which the radar can capture. To estimate the PPG waveform, VitaNet uses a self-attention architecture to identify the most informative reflections in an unsupervised manner, and then uses an encoder decoder network to transform the radar phase profile to the PPG sequence. We have trained and extensively evaluated VitaNet on a large dataset obtained from 25 participants over 179 full nights. Our evaluations show that VitaNet accurately estimates the PPG waveform and its derivatives with high accuracy, significantly improves the heart rate and heart rate variability estimates from the prior works, and also accurately estimates several useful PPG features. We have released the codes of VitaNet as well as the trained models and the dataset used in this paper.

A Review and Tutorial on Machine Learning-Enabled Radar-Based Biomedical Monitoring

“…Several examples in the literature have extracted small body movements from raw radar data to predict vital signs, such as respiration and heart sounds, which are mostly recorded with the radar phase profile over time. For instance, Khan et al [88] used raw radar data for the contactless monitoring of photoplethysmography (PPG) by measuring chest and heart movements. To achieve this, they used a self-attention DL network with an encoder-decoder structure to generate a prediction for the PPG waveform.…”

“…Further, precise time synchronization between radar sensors and reference systems needs to be adequately addressed. Long-term synchronization can be achieved by recording a synchronization signal in both the gold standard and target signal [67] or by implementing a synchronization invariant loss function [88]. If the ML task allows it, rough synchronization -e.g., using time-stamps -can be sufficient [60].…”

Machine Learning in RADAR-based Physiological Signals Sensing: A Scoping Review of the Models, Datasets and Metrics

“…We include papers regarding a fine-grained reconstruction of both a physiological signal or simplified waveform derived from a physiological signal. We find papers about the reconstruction of the electrocardiogram (ECG) (n = 3) [32], [36], [63], the seismocardiogram (SCG) [44] (n = 1) and the photoplethismogram (PPG) (n = 2) [69], [72]; then we find papers dealing with respiratory waveform (n = 4) [68], [71], [90], [94] or simplified version of the cardiac waveform (n = 4) [35], [41], [60], [73] or both (n = 2) [30], [77].…”

“…From Table V, we can notice that the authors made an effort to make the dataset balanced between the two sex. The synchronisation between the RADAR recording and the reference is solved by different methods, such as Precision Time Protocol [30], [68], [94], custom LABVIEW program [73], [100], post-processing manual synchronisation based on clearly defined time events [107], binary shared sequences [78], [108] or by matching of the time stamps [17], [40], [65], [66], [72], [87]. Moreover, we find two methods used to deal with synchronisation errors.…”

“…The first exploits the robustness against synchronisation issues of the CNN-based parallel architectures, and it was proposed to compensate for the synchronisation issues by taking as input different delayed version of the same RADAR data [41]. Moreover, a timeinvariant loss was proposed to correct for any error in the synchronization process for a PPG signal reconstruction task [72].…”