Non-Contact Acquisition of PPG Signal using Chest Movement-Modulated Radio Signals

TL;DR

This study introduces a non-contact method to acquire PPG by leveraging chest movement-modulated reflections of microwave radio signals. It builds the Radio-PPG-16 dataset (160 minutes from 16 subjects) using dual USRP SDRs at 5.24 GHz and pairs reflected radio data with ground-truth PPG from MAX86150. A two-phase pipeline—rigorous radio/PPG pre-processing followed by frequency-domain regression using $DCT$ (ridge or MLP) and inverse $DCT$—maps radio-derived features to synthetic PPG, achieving a test MAE of $8.1294$ while preserving vital waveform characteristics. This approach offers a privacy-preserving, contactless avenue for cardiovascular and respiratory monitoring, with future work aimed at optimizing sub-carrier fusion to further boost performance.

Abstract

We present for the first time a novel method that utilizes the chest movement-modulated radio signals for non-contact acquisition of the photoplethysmography (PPG) signal. Under the proposed method, a software-defined radio (SDR) exposes the chest of a subject sitting nearby to an orthogonal frequency division multiplexing signal with 64 sub-carriers at a center frequency 5.24 GHz, while another SDR in the close vicinity collects the modulated radio signal reflected off the chest. This way, we construct a custom dataset by collecting 160 minutes of labeled data (both raw radio data as well as the reference PPG signal) from 16 healthy young subjects. With this, we first utilize principal component analysis for dimensionality reduction of the radio data. Next, we denoise the radio signal and reference PPG signal using wavelet technique, followed by segmentation and Z-score normalization. We then synchronize the radio and PPG segments using cross-correlation method. Finally, we proceed to the waveform translation (regression) task, whereby we first convert the radio and PPG segments into frequency domain using discrete cosine transform (DCT), and then learn the non-linear regression between them. Eventually, we reconstruct the synthetic PPG signal by taking inverse DCT of the output of regression block, with a mean absolute error of 8.1294. The synthetic PPG waveform has a great clinical significance as it could be used for non-contact performance assessment of cardiovascular and respiratory systems of patients suffering from infectious diseases, e.g., covid19.

Figures (5)

• Figure 1: The proposed method for non-contact PPG monitoring: The experimental setup consists of an SDR pair whereby the transmit SDR exposes the chest of a subject with an OFDM signal, while the receive SDR collects the reflected signal and feeds it to a purpose-built data pre-processing + deep learning pipeline that ultimately synthesizes the PPG signal.
• Figure 2: Top fig. shows a pair of radio and PPG segments after DWT stage but before alignment, while the bottom fig. shows the situation after alignment.
• Figure 3: Complete data pre-processing + regression pipeline for PPG waveform synthesis from the radio signal.
• Figure 4: Training and validation losses of the proposed MLP regressor undergo a steady decrease with the increase in number of epochs.
• Figure 5: Two examples of synthetic PPG reconstruction from the radio data.