Detection of Heartbeat Components Through Doppler Radar Systems Using Semantic Segmentation and Non-Harmonic Analysis

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to an increased focus on the routine analysis of vital signs such as breathing and pulse rates. Radar technology has proven effective for non-contact, long-term monitoring of these vital signs, with frequency analysis being the default method for processing signals from Doppler radar owing to their inherent noise. However, conventional analysis approaches often struggle to detect weak signals buried within the sidelobes of other signals. Some data analysis techniques for Doppler radar rely on machine learning, but they struggle to generate clear time-frequency diagrams, complicating heartbeat detection. In this study, we employed non-harmonic analysis (NHA) as a frequency analysis method to mitigate sidelobe interference and implemented semantic segmentation for precise heartbeat detection. To validate the proposed approach, we conducted heartbeat detection tests both in stationary, low-noise conditions and in a noisy driving simulation environment. The results indicated that the NHA method successfully analyzed heartbeat harmonics, suggesting its potential for detecting heartbeat components through machine learning. To validate these findings, we determined the detection accuracy by comparing true and false positive rates, allowing us to quantify the detectability of heartbeats under both resting and driving simulation conditions.