{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T03:46:16Z","timestamp":1766720776477,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T00:00:00Z","timestamp":1733961600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JFMK"],"abstract":"<jats:p>Background\/Objectives: The purpose of this research was to create a peak detection algorithm and machine learning model for use in triathlon. The algorithm and model aimed to automatically measure movement cadence in all three disciplines of a triathlon using data from a single inertial measurement unit and to recognise the occurrence and duration of cycling task changes. Methods: Six triathletes were recruited to participate in a triathlon while wearing a single trunk-mounted measurement unit and were filmed throughout. Following an initial analysis, a further six triathletes were recruited to collect additional cycling data to train the machine learning model to more effectively recognise cycling task changes. Results: The peak-counting algorithm successfully detected 98.7% of swimming strokes, with a root mean square error of 2.7 swimming strokes. It detected 97.8% of cycling pedal strokes with a root mean square error of 9.1 pedal strokes, and 99.4% of running strides with a root mean square error of 1.2 running strides. Additionally, the machine learning model was 94% (\u00b15%) accurate at distinguishing between \u2018in-saddle\u2019 and \u2018out-of-saddle\u2019 riding, but it was unable to distinguish between \u2018in-saddle\u2019 riding and \u2018coasting\u2019 based on tri-axial acceleration and angular velocity. However, it displayed poor sensitivity to detect \u2018out-of-saddle\u2019 efforts in uncontrolled conditions which improved when conditions were further controlled. Conclusions: A custom peak detection algorithm and machine learning model are effective tools to automatically analyse triathlon performance.<\/jats:p>","DOI":"10.3390\/jfmk9040269","type":"journal-article","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T03:52:49Z","timestamp":1733975569000},"page":"269","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Automatic Recognition of Motor Skills in Triathlon: A Novel Tool for Measuring Movement Cadence and Cycling Tasks"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8444-5933","authenticated-orcid":false,"given":"Stuart M.","family":"Chesher","sequence":"first","affiliation":[{"name":"Curtin School of Allied Health, Curtin University, Kent Street, Bentley, Perth, WA 6102, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carlo","family":"Martinotti","sequence":"additional","affiliation":[{"name":"Curtin Institute for Computation, Curtin University, Kent Street, Bentley, Perth, WA 6102, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3393-1261","authenticated-orcid":false,"given":"Dale W.","family":"Chapman","sequence":"additional","affiliation":[{"name":"Curtin School of Allied Health, Curtin University, Kent Street, Bentley, Perth, WA 6102, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simon M.","family":"Rosalie","sequence":"additional","affiliation":[{"name":"SR Performance, Gairloch Drive, Frankston, Melbourne, VIC 3199, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paula C.","family":"Charlton","sequence":"additional","affiliation":[{"name":"Australian Institute of Sport, Leverrier Street, Bruce, Canberra, ACT 2617, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3680-3548","authenticated-orcid":false,"given":"Kevin J.","family":"Netto","sequence":"additional","affiliation":[{"name":"Curtin School of Allied Health, Curtin University, Kent Street, Bentley, Perth, WA 6102, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102249","DOI":"10.1016\/j.psychsport.2022.102249","article-title":"A qualitative exploration of the motor skills required for elite triathlon performance","volume":"62","author":"Chesher","year":"2022","journal-title":"Psychol. 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