Heart Rate Estimation from Wrist-Type Photoplethysmographic Signals Corrupted by Intense Motion Artifacts using NLMS Adaptive Filter and Spectral Peak Tracking

Current advances in sensor and microelectronics technology have enable real-time non-invasive Heart Rate (HR) monitoring on wearable devices. Wrist-type Photoplethysmography is one of the most widely used methods to estimate HR in wearable devices. However, Photoplethysmographic (PPG) signals are strongly affected by Motion Artifacts (MA) caused by body movements. This paper presents a method of estimating heart rate from wrist-type photoplethysmographic signals during fast running of increasing speed peaking at 15km/hour. Our proposed method successfully suppresses the motion artifacts by using Normalized Least Mean Square (NLMS) adaptive filter and Spectral Peak Tracking (SPT). The proposed method was tested on datasets recorded from 12 subjects available from IEEE Signal Processing Cup 2015 [7]. Our algorithm produces HR estimations with mean absolute error of 1.57 beat per minute and the standard deviation of 1.11 beat per minute.