The JOSS algorithm significantly reduced the average absolute estimation error for heart rate monitoring during physical activities to 1.28 beats per minute compared to 2.42 beats per minute with the TROIKA algorithm.
The JOSS algorithm provides highly accurate heart rate estimation from PPG signals during physical activities by effectively removing motion artifacts, significantly outperforming the existing TROIKA algorithm.
Absolute Event Rate: 1.28% vs 2.42%
p-value: p=6.3 × 10−39
GOAL: A new method for heart rate monitoring using photoplethysmography (PPG) during physical activities is proposed. METHODS: It jointly estimates the 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 the spectral peaks of motion artifact (MA) in the 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/min and the standard deviation was 2.61 beat/min. 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.
Zhilin Zhang (Tue,) conducted a other in Healthy subjects (physical activities) (n=12). JOSS (JOint Sparse Spectrum reconstruction) algorithm vs. TROIKA algorithm was evaluated on Average absolute estimation error of heart rate (BPM) (p=6.3 × 10−39). The JOSS algorithm significantly reduced the average absolute estimation error for heart rate monitoring during physical activities to 1.28 beats per minute compared to 2.42 beats per minute with the TROIKA algorithm.