Millimeter Wave(mmWave) radar has been widely used in vital sign monitoring with non-contact and privacy protection. To reduce the effect of random body motion, respiration, and its harmonics on heart rate estimation, the heart rate monitoring based on frequency-modulated continuous-wave (FMCW) radar is researched in this paper. First, a vital sign signal extraction algorithm is designed for the Range Bin variation caused by random body motion and heavy breathing. Second, a heartbeat signal extraction algorithm is designed based on Adaptive Notch Filter (ANF) and Empirical Wavelet Transform (EWT). The harmonic of respiration will be suppressed, and the heartbeat signal will be separated. Finally, the weighted estimation is performed according to the relationship of harmonics of the heartbeat signal to obtain the heart rate. Twenty subjects are invited to the experiment. The experimental results show that the proposed method can improve the signal-to-noise ratio (SNR), reduce the harmonic interference, and estimate the heart rate with a mean absolute error less than 4 BPM.

Action quality assessment (AQA) is an important problem in computer vision applications. During human AQA, differences in body size or changes in position relative to the sensor may cause unwanted effects. We propose a motion registration method based on self-coordination (SC) and self-referential normalization (SRN). By establishing a coordinate system on the human body and using a part of the human body as a normalized reference standard to process the raw data, the standardization and distinguishability of the raw data are improved. To demonstrate the effectiveness of our method, we conducted experiments on KTH datasets. The experimental results show that the method improved the classification accuracy of the KNN-DTW network for KTH-5 from 82.46% to 87.72% and for KTH-4 from 89.47% to 94.74%, and it improved the classification accuracy of the tsai-MiniRocket network for KTH-5 from 91.29% to 93.86% and for KTH-4 from 94.74% to 97.90%. The results show that our method can reduce the above effects and improve the action classification accuracy of the action classification network. This study provides a new method and idea for improving the accuracy of AQA-related algorithms.