Enhance Heart Rate Measurement from Remote PPG with Head Motion Awareness from Image

Measurement of cardiac pulse rate through image-based remote photoplethysmography (rPPG) is drawing attention to applications of continuous health monitoring. Meanwhile, extracting clean rPPG signals and reliable heart rate (HR) remotely is challenging especially in real-life scenarios where users can move freely. In this paper, we leverage head motion information in the video to increase tolerance of vital estimation against the motion. A motion artifact classification model relying on rPPG and real-time head motion signals is developed to identify motion artifacts and reject outliers. We handcrafted 106 features and selected 20 features from both time and frequency domains. The model and methodology are validated comprehensively in a dataset of 30 subjects with 25 motion tasks in three motion intensity levels: low-motion, medium-motion, and high-motion. The motion-aware pipeline achieves a mean absolute error of 4.03 bpm for high-motion intensity tasks, improved by 31% by removing artifacts with specificity over 75%. In addition, the pipeline is tested with various light intensities to show that the motion detection is robust in darker conditions.