A Novel Scheme for Suppression of Human Motion Effects in Non-Contact Heart Rate Detection

The technology of non-contact heart rate detection has been proven of great use. However, it is still limited by several challenges, particularly the influence of human motion and random disturbances, which significantly degrade the accuracy of the measurement. To address these challenges, this paper proposes a novel scheme for heart rate detection. Firstly, the proposed scheme utilizes the Multi-Channel Averaging (MCA) technique to improve the signal-to-noise ratio (SNR) of the extracted phase signal from the echoes in the multiple receivers, and a high-pass filter to roughly extract a heartbeat signal. Secondly, to suppress motion artifacts (MA) in the heartbeat signal caused by human motion, the Adaptive Parameter Selection for Expectation-Maximization (APSEM) method is proposed. Further, a novel processing sub-framework which combines the Kalman filtering, the Variational Mode Extraction (VME) algorithm, and the Rife spectral analysis method (called KFRV method) is proposed to mitigate the effect of random disturbances and achieve accurate frequency estimation. Experimental results using the Polar H10 heart rate sensor as a reference show that the proposed scheme achieves accurate heart rate detection in the presence of human motion, with the Mean Absolute Error (MAE) of less than 2.5 beats per minute (bpm), which is much better than traditional schemes. Compared to traditional methods, the proposed scheme exhibits negligible loss in heart rate detection under static state, with an average MAE of 0.99 bpm. Overall, the experimental results demonstrate the applicability of the proposed scheme for accurate heart rate detection in both human motion and static states.