{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T19:39:13Z","timestamp":1784921953488,"version":"3.55.0"},"reference-count":27,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2021,1,23]],"date-time":"2021-01-23T00:00:00Z","timestamp":1611360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Research Grants Council of Hong Kong","award":["14209619"],"award-info":[{"award-number":["14209619"]}]},{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"crossref","award":["61672282"],"award-info":[{"award-number":["61672282"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Sen. Netw."],"published-print":{"date-parts":[[2021,5,31]]},"abstract":"<jats:p>Heart rate (HR) estimation based on photoplethysmography (PPG) signals has been widely adopted in wrist-worn devices. However, the motion artifacts caused by the user\u2019s physical activities make it difficult to get the accurate HR estimation from contaminated PPG signals. Although many signal processing methods have been proposed to address this challenge, they are often highly optimized for specific scenarios, making them impractical in real-world settings where a user may perform a wide range of physical activities. In this article, we propose DeepHeart, a new HR estimation approach that features deep-learning-based denoising and spectrum-analysis-based calibration. DeepHeart generates clean PPG signals from electrocardiogram signals based on a training data set. Then a set of denoising convolutional neural networks (DCNNs) are trained with the contaminated PPG signals and their corresponding clean PPG signals. Contaminated PPG signals are then denoised by an ensemble of DCNNs and a spectrum-analysis-based calibration is performed to estimate the final HR. We evaluate DeepHeart on the IEEE Signal Processing Cup training data set with 12 records collected during various physical activities. DeepHeart achieves an average absolute error of 1.61 beats per minute (bpm), outperforming a state-of-the-art deep learning approach (4 bpm) and a classical signal processing approach (2.34 bpm).<\/jats:p>","DOI":"10.1145\/3441626","type":"journal-article","created":{"date-parts":[[2021,1,23]],"date-time":"2021-01-23T11:06:40Z","timestamp":1611400000000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":51,"title":["DeepHeart"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6246-552X","authenticated-orcid":false,"given":"Xiangmao","family":"Chang","sequence":"first","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gangkai","family":"Li","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoliang","family":"Xing","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Zhu","sequence":"additional","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linlin","family":"Tu","sequence":"additional","affiliation":[{"name":"Michigan State University, MI, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,1,23]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICIEV.2016.7760124"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC\u201915)","author":"Ahmadi Amirhosein Khas","unstructured":"Amirhosein Khas Ahmadi , Parsa Moradi , Mahan Malihi , Sajjad Karimi , and Mohammad B. Shamsollahi . 2015. Heart rate monitoring during physical exercise using wrist-type photoplethysmographic (PPG) signals . In Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC\u201915) . IEEE, 6166--6169. Amirhosein Khas Ahmadi, Parsa Moradi, Mahan Malihi, Sajjad Karimi, and Mohammad B. Shamsollahi. 2015. Heart rate monitoring during physical exercise using wrist-type photoplethysmographic (PPG) signals. In Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC\u201915). 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Technical Report, Institute for Brain and Neural Systems, Brown University, Providence, RI."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2011.2175832"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.3390\/s19143079"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.3390\/s16010010"},{"key":"e_1_2_1_21_1","volume-title":"Proceedings of the 23rd European Signal Processing Conference (EUSIPCO\u201915)","author":"Sch\u00e4ck Tim","unstructured":"Tim Sch\u00e4ck , Christian Sledz , Michael Muma , and Abdelhak M. Zoubir . 2015. A new method for heart rate monitoring during physical exercise using photoplethysmographic signals . In Proceedings of the 23rd European Signal Processing Conference (EUSIPCO\u201915) . IEEE, 2666--2670. Tim Sch\u00e4ck, Christian Sledz, Michael Muma, and Abdelhak M. Zoubir. 2015. A new method for heart rate monitoring during physical exercise using photoplethysmographic signals. 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