{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T21:54:42Z","timestamp":1769550882061,"version":"3.49.0"},"reference-count":30,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Universidad de Las Am\u00e9ricas\u2014Ecuador","award":["A.XIV.24"],"award-info":[{"award-number":["A.XIV.24"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Low back pain (LBP) is one of the leading causes of disability in the world's population, yet there are limitations in providing an objective clinical assessment due to its widespread nature. In this work, five machine learning models (LightGBM, XGBoost, HistGradientBoosting, GradientBoosting, and StackingRegressor) were compared to predict trunk mobility based on inertial sensor data. There were 77 individuals with a total of 2160 movement samples of flexion\u2013extension, rotation, and lateralization. Synthetic data augmentation and normalization were performed to be able to work with the data efficiently. Mean absolute error (MAE), mean square error (MSE), and R2 were used to evaluate model performance. Additionally, ANOVA and Tukey\u2019s HSD were used to assess the statistical significance of the models. GradientBoostingRegressor was found to produce the lowest error and statistical significance in flexion\u2013extension and lateralization, while StackingRegressor produced the best error in rotation. The results indicate that inertial sensors and machine learning (ML) can be applied to predict mobility, facilitating personalized rehabilitation and reducing costs. The present study demonstrates that predictive trunk motion modeling can facilitate clinical monitoring and help reduce socioeconomic limitations in patients.<\/jats:p>","DOI":"10.3390\/s25216665","type":"journal-article","created":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T18:21:46Z","timestamp":1762194106000},"page":"6665","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["InertialMov: Machine Learning Test Based on Inertial Sensors to Predict Mobility Impairment in Low Back Pain Patients"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3690-1962","authenticated-orcid":false,"given":"Jeremy","family":"Carlosama","sequence":"first","affiliation":[{"name":"School of Biological Sciences and Engineering, Yachay Tech University, Urcuqu\u00ed 100119, Ecuador"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6505-614X","authenticated-orcid":false,"given":"Luis","family":"Zhinin-Vera","sequence":"additional","affiliation":[{"name":"LoUISE Research Group, University of Castilla-La Mancha, 02005 Albacete, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1571-5829","authenticated-orcid":false,"given":"Cesar","family":"Guevara","sequence":"additional","affiliation":[{"name":"Quantitative Methods Department, CUNEF Universidad, 28040 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5199-5883","authenticated-orcid":false,"given":"Carolina","family":"Cadena-Morej\u00f3n","sequence":"additional","affiliation":[{"name":"School of Mathematical and Computational Science, Yachay Tech University, Urcuqu\u00ed 100119, Ecuador"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9196-335X","authenticated-orcid":false,"given":"Diego","family":"Almeida-Gal\u00e1rraga","sequence":"additional","affiliation":[{"name":"School of Biological Sciences and Engineering, Yachay Tech University, Urcuqu\u00ed 100119, Ecuador"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4855-4796","authenticated-orcid":false,"given":"Lenin","family":"Ram\u00edrez-Cando","sequence":"additional","affiliation":[{"name":"School of Biological Sciences and Engineering, Yachay Tech University, Urcuqu\u00ed 100119, Ecuador"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9861-0039","authenticated-orcid":false,"given":"Kevin R.","family":"Land\u00e1zuri","sequence":"additional","affiliation":[{"name":"School of Biological Sciences and Engineering, Yachay Tech University, Urcuqu\u00ed 100119, Ecuador"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5368-4122","authenticated-orcid":false,"given":"Andr\u00e9s","family":"Tirado-Esp\u00edn","sequence":"additional","affiliation":[{"name":"School of Mathematical and Computational Science, Yachay Tech University, Urcuqu\u00ed 100119, Ecuador"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4210-0117","authenticated-orcid":false,"given":"Patricia","family":"Acosta-Vargas","sequence":"additional","affiliation":[{"name":"Intelligent and Interactive Systems Laboratory, Universidad de Las Am\u00e9ricas, Quito 170125, Ecuador"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7236-7499","authenticated-orcid":false,"given":"Fernando","family":"Villalba-Meneses","sequence":"additional","affiliation":[{"name":"School of Biological Sciences and Engineering, Yachay Tech University, Urcuqu\u00ed 100119, Ecuador"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bahej, I., Clay, I., Jaggi, M., and De Luca, V. 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