{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T16:17:55Z","timestamp":1780589875901,"version":"3.54.1"},"reference-count":107,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,14]],"date-time":"2022-02-14T00:00:00Z","timestamp":1644796800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council","doi-asserted-by":"publisher","award":["STPGP\/506894-2017"],"award-info":[{"award-number":["STPGP\/506894-2017"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Nowadays, wearables-based Human Activity Recognition (HAR) systems represent a modern, robust, and lightweight solution to monitor athlete performance. However, user data variability is a problem that may hinder the performance of HAR systems, especially the cross-subject HAR models. Such a problem may have a lesser effect on the subject-specific model because it is a tailored model that serves a specific user; hence, data variability is usually low, and performance is often high. However, such a performance comes with a high cost in data collection and processing per user. Therefore, in this work, we present a personalized model that achieves higher performance than the cross-subject model while maintaining a lower data cost than the subject-specific model. Our personalization approach sources data from the crowd based on similarity scores computed between the test subject and the individuals in the crowd. Our dataset consists of 3750 concentration curl repetitions from 25 volunteers with ages and BMI ranging between 20\u201346 and 24\u201346, respectively. We compute 11 hand-crafted features and train 2 personalized AdaBoost models, Decision Tree (AdaBoost-DT) and Artificial Neural Networks (AdaBoost-ANN), using data from whom the test subject shares similar physical and single traits. Our findings show that the AdaBoost-DT model outperforms the cross-subject-DT model by 5.89%, while the AdaBoost-ANN model outperforms the cross-subject-ANN model by 3.38%. On the other hand, at 50.0% less of the test subject\u2019s data consumption, our AdaBoost-DT model outperforms the subject-specific-DT model by 16%, while the AdaBoost-ANN model outperforms the subject-specific-ANN model by 10.33%. Yet, the subject-specific models achieve the best performances at 100% of the test subjects\u2019 data consumption.<\/jats:p>","DOI":"10.3390\/s22041454","type":"journal-article","created":{"date-parts":[[2022,2,14]],"date-time":"2022-02-14T20:58:03Z","timestamp":1644872283000},"page":"1454","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Toward the Personalization of Biceps Fatigue Detection Model for Gym Activity: An Approach to Utilize Wearables\u2019 Data from the Crowd"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3131-1448","authenticated-orcid":false,"given":"Mohamed","family":"Elshafei","sequence":"first","affiliation":[{"name":"Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3G 1M8, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7084-2594","authenticated-orcid":false,"given":"Diego Elias","family":"Costa","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3G 1M8, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emad","family":"Shihab","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3G 1M8, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1113\/jphysiol.2007.139477","article-title":"Muscle fatigue: What, why and how it influences muscle function","volume":"586","author":"Enoka","year":"2008","journal-title":"J. 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