{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T18:34:24Z","timestamp":1777401264674,"version":"3.51.4"},"reference-count":114,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T00:00:00Z","timestamp":1652313600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"INAIL","award":["No. 871237"],"award-info":[{"award-number":["No. 871237"]}]},{"name":"SOPHIA project","award":["No. 871237"],"award-info":[{"award-number":["No. 871237"]}]},{"name":"The European Union\u2019s Horizon 2020 research and innovation program","award":["No. 871237"],"award-info":[{"award-number":["No. 871237"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The aim of this study was to determine which supervised machine learning (ML) algorithm can most accurately classify people with Parkinson\u2019s disease (pwPD) from speed-matched healthy subjects (HS) based on a selected minimum set of IMU-derived gait features. Twenty-two gait features were extrapolated from the trunk acceleration patterns of 81 pwPD and 80 HS, including spatiotemporal, pelvic kinematics, and acceleration-derived gait stability indexes. After a three-level feature selection procedure, seven gait features were considered for implementing five ML algorithms: support vector machine (SVM), artificial neural network, decision trees (DT), random forest (RF), and K-nearest neighbors. Accuracy, precision, recall, and F1 score were calculated. SVM, DT, and RF showed the best classification performances, with prediction accuracy higher than 80% on the test set. The conceptual model of approaching ML that we proposed could reduce the risk of overrepresenting multicollinear gait features in the model, reducing the risk of overfitting in the test performances while fostering the explainability of the results.<\/jats:p>","DOI":"10.3390\/s22103700","type":"journal-article","created":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T23:08:36Z","timestamp":1652396916000},"page":"3700","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":116,"title":["Machine Learning Approach to Support the Detection of Parkinson\u2019s Disease in IMU-Based Gait Analysis"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4739-3441","authenticated-orcid":false,"given":"Dante","family":"Trabassi","sequence":"first","affiliation":[{"name":"Department of Medical and Surgical Sciences and Biotechnologies, \u201cSapienza\u201d University of Rome, 04100 Latina, Italy"}]},{"given":"Mariano","family":"Serrao","sequence":"additional","affiliation":[{"name":"Department of Medical and Surgical Sciences and Biotechnologies, \u201cSapienza\u201d University of Rome, 04100 Latina, Italy"},{"name":"Movement Analysis Laboratory, Policlinico Italia, 00162 Rome, Italy"}]},{"given":"Tiwana","family":"Varrecchia","sequence":"additional","affiliation":[{"name":"Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Monte Porzio Catone, 00078 Rome, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0197-6166","authenticated-orcid":false,"given":"Alberto","family":"Ranavolo","sequence":"additional","affiliation":[{"name":"Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Monte Porzio Catone, 00078 Rome, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8510-6925","authenticated-orcid":false,"given":"Gianluca","family":"Coppola","sequence":"additional","affiliation":[{"name":"Department of Medical and Surgical Sciences and Biotechnologies, \u201cSapienza\u201d University of Rome, 04100 Latina, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9415-4948","authenticated-orcid":false,"given":"Roberto","family":"De Icco","sequence":"additional","affiliation":[{"name":"Headache Science & Neurorehabilitation Center, IRCCS Mondino Foundation, 27100 Pavia, Italy"},{"name":"Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1513-2113","authenticated-orcid":false,"given":"Cristina","family":"Tassorelli","sequence":"additional","affiliation":[{"name":"Headache Science & Neurorehabilitation Center, IRCCS Mondino Foundation, 27100 Pavia, Italy"},{"name":"Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5329-5197","authenticated-orcid":false,"given":"Stefano Filippo","family":"Castiglia","sequence":"additional","affiliation":[{"name":"Department of Medical and Surgical Sciences and Biotechnologies, \u201cSapienza\u201d University of Rome, 04100 Latina, Italy"},{"name":"Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Buckley, C., Alcock, L., McArdle, R., Ur Rehman, R.Z., Del Din, S., Mazz\u00e0, C., Yarnall, A.J., and Rochester, L. 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