HeRe: Heartbeat Signal Reconstruction for Low-Power Millimeter-Wave Radar Based on Deep Learning

Abstract

The non-contact monitoring of vital signs without body-attached sensors is a promising home healthcare technique. Recent studies have shown that millimeter-wave (mmW) radar can be used to continuous daily monitoring of vital signs such as heart rate (HR) and respiratory rate (RR). To minimize disruption to the user’s daily life and accommodate for home environments, low-power radar or radar with antennas in package (AiP) is becoming increasingly popular. However, the lower transmitting power of these systems can lead to decreased Signal-to-Noise Ratio (SNR) of the radar signal, making established measurement methods ineffective. The vital signs, particularly HR, are no longer accurately measured due to the small amplitude associated with heartbeats. In this work, we present HeRe for low-power radar, a deep learning approach for heartbeat signal reconstruction. The proposed method utilizes a neural network to identify patterns in the signal, leading to a significant improvement in the SNR of the heartbeat signal. By leveraging historical data, the current HR estimation is further refined, yielding improved precision in the results. Experiments have demonstrated the efficacy of signal reconstruction for improving the heartbeat signal in both the time and frequency domains. A comparison of the proposed approach to related works on various HR distributions, sensing ranges, and subjects showed significantly higher accuracy. To further assess the robustness of the proposed approach, extended continuous monitoring was performed and 97.5% accuracy was achieved. This article highlights the advantages of deep learning techniques for non-contact, radar-based HR monitoring, leading to accurate HR monitoring with the lowest power consumer-grade millimeter wave radar currently available on the market.