A Motion-Tolerant Adaptive Algorithm for Wearable Photoplethysmographic Biosensors

The performance of portable and wearable biosensors is highly influenced by motion artifact. In this paper, a novel real-time adaptive algorithm is proposed for accurate motion-tolerant extraction of heart rate (HR) and pulse oximeter oxygen saturation (SpO(2)) from wearable photoplethysmographic (PPG) biosensors. The proposed algorithm removes motion artifact due to various sources including tissue effect and venous blood changes during body movements and provides noise-free PPG waveforms for further feature extraction. A two-stage normalized least mean square adaptive noise canceler is designed and validated using a novel synthetic reference signal at each stage. Evaluation of the proposed algorithm is done by Bland-Altman agreement and correlation analyses against reference HR from commercial ECG and SpO(2) sensors during standing, walking, and running at different conditions for a single-and multisubject scenarios. Experimental results indicate high agreement and high correlation (more than 0.98 for HR and 0.7 for SpO(2) extraction) between measurements by reference sensors and our algorithm.