{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/computationally-efficient-heart-rate","title":"Computationally efficient heart rate estimation during physical exercise using photoplethysmographic signals","arxiv_id":null,"date":"2017-08-28","proceeding":"2017 25th European Signal Processing Conference (EUSIPCO) 2017 8","authors":["Tim Schäck","Michael Muma","Abdelhak M. Zoubir"],"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.","url_abs":"https://doi.org/10.23919/EUSIPCO.2017.8081656","url_pdf":"https://www.researchgate.net/publication/319176582_Computationally_Efficient_Heart_Rate_Estimation_During_Physical_Exercise_Using_Photoplethysmographic_Signals","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"heart-rate-estimation","task_name":"Heart rate estimation"},{"task_slug":"photoplethysmography-ppg","task_name":"Photoplethysmography (PPG)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/heart-rate-estimation-on-ppg-dalia","task":"Heart rate estimation","dataset":"PPG-DaLiA","model":"Schäck2017","rank_in_archive_order":6,"of":6,"metrics":{"MAE [bpm, session-wise]":"20.45 ± 7.1"},"uses_additional_data":false},{"leaderboard":"/sota/heart-rate-estimation-on-wesad","task":"Heart rate estimation","dataset":"WESAD","model":"Schäck2017","rank_in_archive_order":5,"of":5,"metrics":{"MAE [bpm, session-wise]":"19.97 ± 8.1"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}