Photoplethysmography-Based Heart Rate Monitoring in Physical Activities via Joint Sparse Spectrum Reconstruction

TL;DR

This paper introduces a novel joint sparse spectrum reconstruction method for PPG-based heart rate monitoring during physical activities, effectively removing motion artifacts without extra processing.

Contribution

It proposes a joint spectral estimation approach that leverages common sparsity to improve heart rate detection in noisy PPG signals during exercise.

Findings

Achieved an average error of 1.28 bpm in heart rate estimation.

Demonstrated high performance during fast running activities.

Eliminated need for additional motion artifact removal modules.

Abstract

Goal: A new method for heart rate monitoring using photoplethysmography (PPG) during physical activities is proposed. Methods: It jointly estimates spectra of PPG signals and simultaneous acceleration signals, utilizing the multiple measurement vector model in sparse signal recovery. Due to a common sparsity constraint on spectral coefficients, the method can easily identify and remove spectral peaks of motion artifact (MA) in PPG spectra. Thus, it does not need any extra signal processing modular to remove MA as in some other algorithms. Furthermore, seeking spectral peaks associated with heart rate is simplified. Results: Experimental results on 12 PPG datasets sampled at 25 Hz and recorded during subjects' fast running showed that it had high performance. The average absolute estimation error was 1.28 beat per minute and the standard deviation was 2.61 beat per minute. Conclusion and Significance: These results show that the method has great potential to be used for PPG-based heart rate monitoring in wearable devices for fitness tracking and health monitoring.