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Computationally efficient heart rate estimation during physical exercise using photoplethysmographic signals
Tim Schäck, Michael Muma, Abdelhak M. Zoubir
Wearable devices that acquire photoplethysmographic (PPG) signals are becoming increasingly popular to monitor the heart rate during physical exercise. However, high accuracy and low computational complexity are conflicting requirements. We propose a method that provides highly accurate heart rate estimates at a very low computational cost in order to be implementable on wearables. To achieve the lowest possible complexity, only basic signal processing operations, i.e., correlation-based fundamental frequency estimation and spectral combination, harmonic noise damping and frequency domain tracking, are used. The proposed approach outperforms state-of-the-art methods on current benchmark data considerably in terms of computation time, while achieving a similar accuracy.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Heart rate estimation | PPG-DaLiA | Schäck2017 | MAE [bpm, session-wise] | 20.45 ± 7.1 | #6 of 6 | Archive leaderboard | report |
| Heart rate estimation | WESAD | Schäck2017 | MAE [bpm, session-wise] | 19.97 ± 8.1 | #5 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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