Computationally efficient heart rate estimation during physical exercise using photoplethysmographic signals. Schäck, T., Muma, M., & Zoubir, A. M. In 2017 25th European Signal Processing Conference (EUSIPCO), pages 2478-2481, Aug, 2017.
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Paper doi abstract bibtex
Paper doi abstract bibtex 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.
@InProceedings{8081656,
author = {T. Schäck and M. Muma and A. M. Zoubir},
booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
title = {Computationally efficient heart rate estimation during physical exercise using photoplethysmographic signals},
year = {2017},
pages = {2478-2481},
abstract = {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.},
keywords = {body sensor networks;electrocardiography;frequency estimation;medical signal processing;patient monitoring;photoplethysmography;signal denoising;fundamental frequency estimation;harmonic noise damping;frequency domain tracking;physical exercise;wearable devices;PPG;wearables;heart rate estimation;photoplethysmographic signals;computational complexity;signal processing operations;heart rate;correlation-based fundamental frequency estimation;spectral combination;Heart rate;Motion artifacts;Estimation;Biomedical monitoring;Acceleration;Computational complexity;Photoplethysmography (PPG);Heart Rate Estimation;Motion Artifacts (MA)},
doi = {10.23919/EUSIPCO.2017.8081656},
issn = {2076-1465},
month = {Aug},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570346964.pdf},
}Downloads: 0
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