{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T20:24:13Z","timestamp":1761164653580,"version":"build-2065373602"},"reference-count":11,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T00:00:00Z","timestamp":1733961600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Internet Technology Letters"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    This study introduces a novel dual\u2010context Long Short\u2010Term Memory (LSTM) model for personalized exercise heart rate estimation and distribution analysis based on wearable devices, addressing the limitations of existing wearable\u2010based methods in capturing the complex, user\u2010specific heart rate dynamics. Leveraging a large\u2010scale dataset with over 250\u2009000 workout records, our model incorporates both immediate exercise context and historical user data through a multi\u2010task learning framework. The architecture features personalized embedding layers accounting for static user attributes and dynamic exercise history, significantly enhancing individualized estimations. Experimental results show that the model outperforms baseline methods, achieving a mean absolute error (MAE) of 5.2\u2009bpm and an\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    score of 0.89, with robust predictions across various exercise types. Our analysis reveals insightful patterns in how individual characteristics and exercise history influence heart rate responses, contributing to a deeper understanding of personalized exercise physiology. These findings have important implications for developing more effective personalized fitness recommendations and health monitoring systems, advancing the fields of exercise science and personalized health technology.\n                  <\/jats:p>","DOI":"10.1002\/itl2.627","type":"journal-article","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T20:18:17Z","timestamp":1734034697000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Wearable\u2010Based Personalized Exercise Heart Rate Estimation and Distribution Analysis Using Dual\u2010Context\n                    <scp>LSTM<\/scp>\n                    Model"],"prefix":"10.1002","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9183-0277","authenticated-orcid":false,"given":"Yujie","family":"Fan","sequence":"first","affiliation":[{"name":"Jilin Sport University  Jilin China"}]}],"member":"311","published-online":{"date-parts":[[2024,12,12]]},"reference":[{"issue":"4","key":"e_1_2_8_2_1","first-page":"1825","article-title":"Wearable\u2010Based Assessment of Heart Rate Response to Physical Stressors in Patients After Open\u2010Heart Surgery With Frailty","volume":"27","author":"Sokas D.","year":"2023","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"e_1_2_8_3_1","article-title":"A Review of Deep Learning Methods for Photoplethysmography Data","author":"Nie G.","year":"2024","journal-title":"arXiv Preprint arXiv:2401.12783"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/s18082619"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/10.376135"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph19042417"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104782"},{"key":"e_1_2_8_8_1","first-page":"6237","article-title":"Keeping People Active and Healthy at Home Using a Reinforcement Learning\u2010Based Fitness Recommendation Framework","author":"Tragos E. Z.","year":"2023","journal-title":"IJCAI),"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-023-00926-4"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00773"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2024.3375920"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106591"}],"container-title":["Internet Technology Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/itl2.627","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T18:26:30Z","timestamp":1761071190000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/itl2.627"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,12]]},"references-count":11,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["10.1002\/itl2.627"],"URL":"https:\/\/doi.org\/10.1002\/itl2.627","archive":["Portico"],"relation":{},"ISSN":["2476-1508","2476-1508"],"issn-type":[{"type":"print","value":"2476-1508"},{"type":"electronic","value":"2476-1508"}],"subject":[],"published":{"date-parts":[[2024,12,12]]},"assertion":[{"value":"2024-10-26","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-26","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e627"}}