{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T10:40:50Z","timestamp":1769424050833,"version":"3.49.0"},"reference-count":45,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,21]],"date-time":"2025-01-21T00:00:00Z","timestamp":1737417600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Enterprises and Made in Italy","award":["CUPB87H21012310008"],"award-info":[{"award-number":["CUPB87H21012310008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Standing long jump (SLJ) power is recognized as informative of the ability of lower limbs to exert power. The study aims to provide athletes\/coaches with a simple and low-cost estimate of selected SLJ power features. A group of 150 trained young participants was recruited and performed a SLJ task while holding a smartphone, whose inertial sensors were used to collect data. Considering the state-of-the-art in SLJ biomechanics, a set of features was extracted and then selected by Lasso regression and used as inputs to several different optimized machine learning architectures to estimate the SLJ power variables. A Multi-Layer Perceptron Regressor was selected as the best-performing model to estimate total and concentric antero-posterior mean power, with an RMSE of 0.37 W\/kg, R2 &gt; 0.70, and test phase homoscedasticity (Kendall\u2019s \u03c4 &lt; 0.1) in both cases. Model performance was dependent on the dataset size rather than the participants\u2019 sex. A Multi-Layer Perceptron Regressor was able to also estimate the antero-posterior peak power (RMSE = 2.34 W\/kg; R2 = 0.67), although affected by heteroscedasticity. This study proved the feasibility of combining low-cost smartphone sensors and machine learning to automatically and objectively estimate SLJ power variables in ecological settings.<\/jats:p>","DOI":"10.3390\/computers14020031","type":"journal-article","created":{"date-parts":[[2025,1,21]],"date-time":"2025-01-21T05:47:42Z","timestamp":1737438462000},"page":"31","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Combining Smartphone Inertial Sensors and Machine Learning Algorithms to Estimate Power Variables in Standing Long Jump"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2887-9139","authenticated-orcid":false,"given":"Beatrice","family":"De Lazzari","sequence":"first","affiliation":[{"name":"Department of Movement, Human and Health Science, University of Rome \u201cForo Italico\u201d, Piazza Lauro de Bosis 6, 00135 Roma, LZ, Italy"},{"name":"Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, University of Rome \u201cForo Italico\u201d, Piazza Lauro de Bosis 6, 00135 Roma, LZ, Italy"},{"name":"GoSport s.r.l., Via Basento, Lazio, 00198 Roma, LZ, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2359-6076","authenticated-orcid":false,"given":"Giuseppe","family":"Vannozzi","sequence":"additional","affiliation":[{"name":"Department of Movement, Human and Health Science, University of Rome \u201cForo Italico\u201d, Piazza Lauro de Bosis 6, 00135 Roma, LZ, Italy"},{"name":"Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, University of Rome \u201cForo Italico\u201d, Piazza Lauro de Bosis 6, 00135 Roma, LZ, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7452-120X","authenticated-orcid":false,"given":"Valentina","family":"Camomilla","sequence":"additional","affiliation":[{"name":"Department of Movement, Human and Health Science, University of Rome \u201cForo Italico\u201d, Piazza Lauro de Bosis 6, 00135 Roma, LZ, Italy"},{"name":"Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, University of Rome \u201cForo Italico\u201d, Piazza Lauro de Bosis 6, 00135 Roma, LZ, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,21]]},"reference":[{"key":"ref_1","first-page":"28","article-title":"Development of Standing Long Jump Distance Prediction Models Using Generalized Regression Neural Network","volume":"6","author":"Akay","year":"2018","journal-title":"ICOLES"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1810","DOI":"10.1519\/JSC.0b013e3181ddb03d","article-title":"Assessing Muscular Strength in Youth: Usefulness of Standing Long Jump as a General Index of Muscular Fitness","volume":"24","author":"Ortega","year":"2010","journal-title":"J. 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