{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T19:40:11Z","timestamp":1778269211361,"version":"3.51.4"},"reference-count":45,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2016,1,21]],"date-time":"2016-01-21T00:00:00Z","timestamp":1453334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003407","name":"Ministero dell\u2019Istruzione, dell\u2019Universit\u00e0 e della Ricerca","doi-asserted-by":"publisher","award":["PRIN project \u201cA quantitative and multi-factorial approach for estimating and preventing the risk of falls in the elderly people.\u201d"],"award-info":[{"award-number":["PRIN project \u201cA quantitative and multi-factorial approach for estimating and preventing the risk of falls in the elderly people.\u201d"]}],"id":[{"id":"10.13039\/501100003407","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Machine learning methods have been widely used for gait assessment through the estimation of spatio-temporal parameters. As a further step, the objective of this work is to propose and validate a general probabilistic modeling approach for the classification of different pathological gaits. Specifically, the presented methodology was tested on gait data recorded on two pathological populations (Huntington\u2019s disease and post-stroke subjects) and healthy elderly controls using data from inertial measurement units placed at shank and waist. By extracting features from group-specific Hidden Markov Models (HMMs) and signal information in time and frequency domain, a Support Vector Machines classifier (SVM) was designed and validated. The 90.5% of subjects was assigned to the right group after leave-one-subject\u2013out cross validation and majority voting. The long-term goal we point to is the gait assessment in everyday life to early detect gait alterations.<\/jats:p>","DOI":"10.3390\/s16010134","type":"journal-article","created":{"date-parts":[[2016,1,22]],"date-time":"2016-01-22T11:36:13Z","timestamp":1453462573000},"page":"134","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":218,"title":["A Machine Learning Framework for Gait Classification Using Inertial Sensors: Application to Elderly, Post-Stroke and Huntington\u2019s Disease Patients"],"prefix":"10.3390","volume":"16","author":[{"given":"Andrea","family":"Mannini","sequence":"first","affiliation":[{"name":"The BioRobotics Institute, Scuola Superiore Sant\u2019Anna, Pisa 56127, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diana","family":"Trojaniello","sequence":"additional","affiliation":[{"name":"Information Engineering Unit, POLCOMING Department, University of Sassari, Sassari 07100, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7276-5382","authenticated-orcid":false,"given":"Andrea","family":"Cereatti","sequence":"additional","affiliation":[{"name":"Information Engineering Unit, POLCOMING Department, University of Sassari, Sassari 07100, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3306-6498","authenticated-orcid":false,"given":"Angelo","family":"Sabatini","sequence":"additional","affiliation":[{"name":"The BioRobotics Institute, Scuola Superiore Sant\u2019Anna, Pisa 56127, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,1,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1109\/MEMB.2010.936554","article-title":"Wearable sensors and systems","volume":"29","author":"Bonato","year":"2010","journal-title":"IEEE Eng. 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