Key result
The proposed computationally efficient method for heart rate estimation using PPG signals outperformed state-of-the-art methods in computation time while achieving similar accuracy.
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A novel, computationally efficient algorithm for PPG-based heart rate estimation during exercise offers similar accuracy to existing methods but at a much lower computational cost, making it highly suitable for wearable devices.
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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Schäck et al. (2017) studied Heart rate monitoring during physical exercise. Computationally efficient heart rate estimation method vs. State-of-the-art methods was evaluated on Computation time and accuracy. The proposed computationally efficient method for heart rate estimation using PPG signals outperformed state-of-the-art methods in computation time while achieving similar accuracy.
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