{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T22:42:08Z","timestamp":1790635328381,"version":"4.1.0"},"reference-count":32,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2023,7,20]],"date-time":"2023-07-20T00:00:00Z","timestamp":1689811200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"ANTA","award":["ANTA Sports Award"],"award-info":[{"award-number":["ANTA Sports Award"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Inertial measurement units (IMU) constitute a light and cost-effective alternative to gold-standard measurement systems in the assessment of running temporal variables. IMU data collected on 20 runners running at different speeds (80, 90, 100, 110 and 120% of preferred running speed) and treadmill inclination (\u00b12, \u00b15, and \u00b18%) were used here to predict the following temporal variables: stride frequency, duty factor, and two indices of running variability such as the detrended fluctuation analysis alpha (DFA-\u03b1) and the Higuchi\u2019s D (HG-D). Three different estimation methodologies were compared: 1) a gold-standard optoelectronic device (which provided the reference values), 2) IMU placed on the runner\u2019s feet, 3) a single IMU on the runner\u2019s thorax used in conjunction with a machine learning algorithm with a short 2-second or a long 120-second window as input. A two-way ANOVA was used to test the presence of significant (p&lt;0.05) differences due to the running condition or to the estimation methodology. The findings of this study suggest that using both IMU configurations for estimating stride frequency can be effective and comparable to the gold-standard. Additionally, the results indicate that the use of a single IMU on the thorax with a machine learning algorithm can lead to more accurate estimates of duty factor than the strategy of the IMU on the feet. However, caution should be exercised when using these techniques to measure running variability indices. Estimating DFA-\u03b1 from a short 2-second time window was possible only in level running but not in downhill running and it could not accurately estimate HG-D across all running conditions. By taking a long 120-second window a machine learning algorithm could improve the accuracy in the estimation of DFA-\u03b1 in all running conditions. By taking these factors into account, researchers and practitioners can make informed decisions about the use of IMU technology in measuring running biomechanics.<\/jats:p>","DOI":"10.1371\/journal.pone.0287978","type":"journal-article","created":{"date-parts":[[2023,7,20]],"date-time":"2023-07-20T17:49:20Z","timestamp":1689875360000},"page":"e0287978","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":9,"title":["Indoor running temporal variability for different running speeds, treadmill inclinations, and three different estimation strategies"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1315-5573","authenticated-orcid":true,"given":"Andrea","family":"Zignoli","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antoine","family":"Godin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9486-3090","authenticated-orcid":true,"given":"Laurent","family":"Mourot","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2023,7,20]]},"reference":[{"key":"pone.0287978.ref001","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/14763141.2021.1873411","article-title":"The biomechanics of running and running styles: a synthesis","author":"BT van Oeveren","year":"2021","journal-title":"Sports Biomechanics"},{"key":"pone.0287978.ref002","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1007\/s40279-016-0605-y","article-title":"Biomechanics and Physiology of Uphill and Downhill Running","volume":"47","author":"G Vernillo","year":"2017","journal-title":"Sports Med"},{"key":"pone.0287978.ref003","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1123\/kr.2017-0054","article-title":"Fractal Dynamics, Variability, and Coordination in Human Locomotion","volume":"7","author":"SW Ducharme","year":"2018","journal-title":"Kinesiology Review"},{"key":"pone.0287978.ref004","doi-asserted-by":"crossref","first-page":"333","DOI":"10.3389\/fphys.2020.00333","article-title":"Fractal Analysis of Human Gait Variability via Stride Interval Time Series","volume":"11","author":"A Phinyomark","year":"2020","journal-title":"Front Physiol"},{"key":"pone.0287978.ref005","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1152\/jappl.1995.78.1.349","article-title":"Is walking a random walk? 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