Robust heart rate estimation using wrist-based PPG signals in the presence of intense physical activities

Heart rate tracking from a wrist-type photoplethysmogram (PPG) signal during intensive physical activities is a challenge that is attracting more attention thanks to the introduction of wrist-worn wearable computers. Commonly-used motion artifact rejection methods coupled with simple periodicity-based heart rate estimation techniques are incapable of achieving satisfactory heart rate tracking performance during intense activities. In this paper, we propose a two-stage solution. Firstly, we introduce an improved spectral subtraction method to reject the spectral components of motion artifacts. Secondly, instead of using heuristic mechanisms, we formalize the spectral peaks selection process as the shortest path search problem and validate its effectiveness. Analysis on the experimental results based on a published database shows that: (1) Our proposed method outperforms three other comparable methods with regards to heart rate estimation error. (2) The proposed method is a promising candidate for both offline cardiac health analysis and online heart rate tracking in daily life, even during intensive physical motions.