{"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/online-heart-rate-prediction-using","title":"Online Heart Rate Prediction using Acceleration from a Wrist Worn Wearable","arxiv_id":"1807.04667","date":"2018-06-25","proceeding":null,"authors":["Ryan McConville","Gareth Archer","Ian Craddock","Herman ter Horst","Robert Piechocki","James Pope","Raul Santos-Rodriguez"],"abstract":"In this paper we study the prediction of heart rate from acceleration using a\nwrist worn wearable. Although existing photoplethysmography (PPG) heart rate\nsensors provide reliable measurements, they use considerably more energy than\naccelerometers and have a major impact on battery life of wearable devices. By\nusing energy-efficient accelerometers to predict heart rate, significant energy\nsavings can be made. Further, we are interested in understanding patient\nrecovery after a heart rate intervention, where we expect a variation in heart\nrate over time. Therefore, we propose an online approach to tackle the concept\nas time passes. We evaluate the methods on approximately 4 weeks of free living\ndata from three patients over a number of months. We show that our approach can\nachieve good predictive performance (e.g., 2.89 Mean Absolute Error) while\nusing the PPG heart rate sensor infrequently (e.g., 20.25% of the samples).","url_abs":"http://arxiv.org/abs/1807.04667v1","url_pdf":"http://arxiv.org/pdf/1807.04667v1.pdf","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":[{"paper_slug":"online-heart-rate-prediction-using","repo_url":"https://github.com/rymc/PPAW","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"online-heart-rate-prediction-using","repo_url":"https://github.com/rymc/StreamingEnsembleRegressionForVeryTemporalConceptDriftingDataUsingActiveLearning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"photoplethysmography-ppg","task_name":"Photoplethysmography (PPG)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}