{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T17:11:54Z","timestamp":1776791514402,"version":"3.51.2"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684567","type":"print"},{"value":"9781643684574","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T00:00:00Z","timestamp":1706140800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,25]]},"abstract":"<jats:p>This research aimed to develop a model for real-time prediction of aerobic exercise exertion levels. ECG signals were registered during 16-minute cycling exercises. Perceived ratings of exertion (RPE) were collected each minute from the study participants. Based on the reported RPE, each consecutive minute of the exercise was assigned to the \u201chigh exertion\u201d or \u201clow exertion\u201d class. The characteristics of heart rate variability (HRV) in time and frequency domains were used as predictive features. The top ten ranked predictive features were selected using the minimum redundancy maximum relevance (mRMR) algorithm. The support vector machine demonstrated the highest accuracy with an F1 score of 82%.<\/jats:p>","DOI":"10.3233\/shti231228","type":"book-chapter","created":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T10:29:27Z","timestamp":1706178567000},"source":"Crossref","is-referenced-by-count":6,"title":["Machine Learning Approaches for Exercise Exertion Level Classification Using Data from Wearable Physiologic Monitors"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1077-2229","authenticated-orcid":false,"given":"Aref","family":"Smiley","sequence":"first","affiliation":[{"name":"Center for Biomedical and Population Health Informatics, Icahn School of Medicine at Mount Sinai, New York, USA"}]},{"given":"Te-Yi","family":"Tsai","sequence":"additional","affiliation":[{"name":"Center for Biomedical and Population Health Informatics, Icahn School of Medicine at Mount Sinai, New York, USA"}]},{"given":"Ihor","family":"Havrylchuk","sequence":"additional","affiliation":[{"name":"Center for Biomedical and Population Health Informatics, Icahn School of Medicine at Mount Sinai, New York, USA"}]},{"given":"Aileen","family":"Gabriel","sequence":"additional","affiliation":[{"name":"Center for Biomedical and Population Health Informatics, Icahn School of Medicine at Mount Sinai, New York, USA"}]},{"given":"Elena","family":"Zakashansky","sequence":"additional","affiliation":[{"name":"Center for Biomedical and Population Health Informatics, Icahn School of Medicine at Mount Sinai, New York, USA"}]},{"given":"Taulant","family":"Xhakli","sequence":"additional","affiliation":[{"name":"Center for Biomedical and Population Health Informatics, Icahn School of Medicine at Mount Sinai, New York, USA"}]},{"given":"Jinyan","family":"Lyu","sequence":"additional","affiliation":[{"name":"Center for Biomedical and Population Health Informatics, Icahn School of Medicine at Mount Sinai, New York, USA"}]},{"given":"Wanting","family":"Cui","sequence":"additional","affiliation":[{"name":"Center for Biomedical and Population Health Informatics, Icahn School of Medicine at Mount Sinai, New York, USA"}]},{"given":"Irena","family":"Parvanova","sequence":"additional","affiliation":[{"name":"Center for Biomedical and Population Health Informatics, Icahn School of Medicine at Mount Sinai, New York, USA"}]},{"given":"Joseph","family":"Finkelstein","sequence":"additional","affiliation":[{"name":"Center for Biomedical and Population Health Informatics, Icahn School of Medicine at Mount Sinai, New York, USA"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2023 \u2014 The Future Is Accessible"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI231228","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T10:29:28Z","timestamp":1706178568000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI231228"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,25]]},"ISBN":["9781643684567","9781643684574"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti231228","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,25]]}}}