{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T01:58:56Z","timestamp":1777514336824,"version":"3.51.4"},"reference-count":43,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,6,5]],"date-time":"2020-06-05T00:00:00Z","timestamp":1591315200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Assessing the risk of fall in elderly people is a difficult challenge for clinicians. Since falls represent one of the first causes of death in such people, numerous clinical tests have been created and validated over the past 30 years to ascertain the risk of falls. More recently, the developments of low-cost motion capture sensors have facilitated observations of gait differences between fallers and nonfallers. The aim of this study is twofold. First, to design a method combining clinical tests and motion capture sensors in order to optimize the prediction of the risk of fall. Second to assess the ability of artificial intelligence to predict risk of fall from sensor raw data only. Seventy-three nursing home residents over the age of 65 underwent the Timed Up and Go (TUG) and six-minute walking tests equipped with a home-designed wearable Inertial Measurement Unit during two sets of measurements at a six-month interval. Observed falls during that interval enabled us to divide residents into two categories: fallers and nonfallers. We show that the TUG test results coupled to gait variability indicators, measured during a six-minute walking test, improve (from 68% to 76%) the accuracy of risk of fall\u2019s prediction at six months. In addition, we show that an artificial intelligence algorithm trained on the sensor raw data of 57 participants reveals an accuracy of 75% on the remaining 16 participants.<\/jats:p>","DOI":"10.3390\/s20113207","type":"journal-article","created":{"date-parts":[[2020,6,9]],"date-time":"2020-06-09T05:16:14Z","timestamp":1591679774000},"page":"3207","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":79,"title":["Timed Up and Go and Six-Minute Walking Tests with Wearable Inertial Sensor: One Step Further for the Prediction of the Risk of Fall in Elderly Nursing Home People"],"prefix":"10.3390","volume":"20","author":[{"given":"Fabien","family":"Buisseret","sequence":"first","affiliation":[{"name":"Centre de Recherche et de Formation (CeREF), Chauss\u00e9e de Binche 159, 7000 Mons, Belgium"},{"name":"Haute Ecole Louvain en Hainaut (HELHa), Chauss\u00e9e de Binche 159, 7000 Mons, Belgium"},{"name":"Service de Physique Nucl\u00e9aire et Subnucl\u00e9aire, UMONS, Research Institute for Complex Systems, 20 Place du Parc, 7000 Mons, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0040-7954","authenticated-orcid":false,"given":"Louis","family":"Catinus","sequence":"additional","affiliation":[{"name":"Centre de Recherche et de Formation (CeREF), Chauss\u00e9e de Binche 159, 7000 Mons, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R\u00e9mi","family":"Grenard","sequence":"additional","affiliation":[{"name":"Centre de Recherche et de Formation (CeREF), Chauss\u00e9e de Binche 159, 7000 Mons, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laurent","family":"Jojczyk","sequence":"additional","affiliation":[{"name":"Centre de Recherche et de Formation (CeREF), Chauss\u00e9e de Binche 159, 7000 Mons, Belgium"},{"name":"Haute Ecole Louvain en Hainaut (HELHa), Chauss\u00e9e de Binche 159, 7000 Mons, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dylan","family":"Fievez","sequence":"additional","affiliation":[{"name":"Centre de Recherche et de Formation (CeREF), Chauss\u00e9e de Binche 159, 7000 Mons, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vincent","family":"Barvaux","sequence":"additional","affiliation":[{"name":"Centre de Recherche et de Formation (CeREF), Chauss\u00e9e de Binche 159, 7000 Mons, Belgium"},{"name":"Haute Ecole Louvain en Hainaut (HELHa), Chauss\u00e9e de Binche 159, 7000 Mons, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fr\u00e9d\u00e9ric","family":"Dierick","sequence":"additional","affiliation":[{"name":"Centre de Recherche et de Formation (CeREF), Chauss\u00e9e de Binche 159, 7000 Mons, Belgium"},{"name":"Centre National de R\u00e9\u00e9ducation Fonctionnelle et de R\u00e9adaptation\u2014Rehazenter, Laboratoire d\u2019Analyse du Mouvement et de la Posture (LAMP), 2674 Luxembourg, Luxembourg"},{"name":"Facult\u00e9 des Sciences de la Motricit\u00e9, Universit\u00e9 catholique de Louvain, 1348 Louvain-la-Neuve, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,5]]},"reference":[{"key":"ref_1","unstructured":"(2020, February 12). 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