A framework to improve the performance of heart rate estimation based on facial video during exercise

In recent years, with the wide application of remote photoplethysmography (rPPG) in heart rate (HR) estimation based on facial video. How to effectively overcome the Motion Artifact (MA) to estimate the heart rate in the moving scene has become an urgent problem to be solved. In the paper, a heart rate estimation framework combining adaptive filtering and spectral subtraction is proposed to improve the accuracy of heart rate estimation during exercise. The framework will choose different signal processing methods for different motions, and use the extracted motion information to adaptively select adaptive filtering or spectral subtraction to reduce the noise of the signal. In the final heart rate estimation part, the Hidden Markov Model (HMM) is used to track the frequency trace of the signal in the frequency domain, to improve the robustness of heart rate estimation. We tested the proposed framework for heart rate detection in a variety of exercise scenarios on a publicly available dataset containing a variety of fitness exercises. The experimental results show that the proposed framework can effectively improve heart rate estimation performance during vigorous exercise.