Human Vital Signs Estimation Using Resonance Sparse Spectrum Decomposition

The noncontact measurement and monitoring of human vital signs has evolved into a valuable tool for efficient health management. Because of the greater penetration capability through material and clothes, which is less affected by environmental conditions such as illumination, temperature, and humidity, mmWave radar has been extensively researched for human vital sign measurement in the past years. However, interference due to unwanted clutter, random body movement, and respiration harmonics make accurate retrieval of the heart rate (HR) difficult. This article proposes a resonance sparse spectrum decomposition (RSSD) algorithm and harmonics used algorithm (HUA) for accurate HR extraction. RSSD addresses the clutter and random body movement effects from phase signals, while HUA uses harmonics to extract HR accurately. A set of controlled experiments was conducted under different scenarios, and the proposed method is validated against ground truth HR/RR data collected by a smart vest. Our results show an accuracy of up to 98%–100% for distances up to 2 m. The method substantially improves HR estimation accuracy by effectively mitigating the effects of noise in the phase signal, even under heavy clutter and moderate body movement. Our results demonstrate that the proposed method effectively counters harmonic interference for accurate estimation of HR comparable to RR estimation up to a distance of 4 m from the radar sensor.