{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T16:24:21Z","timestamp":1781367861982,"version":"3.54.1"},"reference-count":40,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,17]],"date-time":"2021-05-17T00:00:00Z","timestamp":1621209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100005217","name":"National Center for Injury Prevention and Control","doi-asserted-by":"publisher","award":["R49 CE002108-05"],"award-info":[{"award-number":["R49 CE002108-05"]}],"id":[{"id":"10.13039\/100005217","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Falls among the elderly population cause detrimental physical, mental, financial problems and, in the worst case, death. The increasing number of people entering the higher risk age-range has increased clinicians\u2019 attention to intervene. Clinical tools, e.g., the Timed Up and Go (TUG) test, have been created for aiding clinicians in fall-risk assessment. Often simple to evaluate, these assessments are subject to a clinician\u2019s judgment. Wearable sensor data with machine learning algorithms were introduced as an alternative to precisely quantify ambulatory kinematics and predict prospective falls. However, they require a long-term evaluation of large samples of subjects\u2019 locomotion and complex feature engineering of sensor kinematics. Therefore, it is critical to build an objective fall-risk detection model that can efficiently measure biometric risk factors with minimal costs. We built and studied a sensor data-driven convolutional neural network model to predict older adults\u2019 fall-risk status with relatively high sensitivity to geriatrician\u2019s expert assessment. The sample in this study is representative of older patients with multiple co-morbidity seen in daily medical practice. Three non-intrusive wearable sensors were used to measure participants\u2019 gait kinematics during the TUG test. This data collection ensured convenient capture of various gait impairment aspects at different body locations.<\/jats:p>","DOI":"10.3390\/s21103481","type":"journal-article","created":{"date-parts":[[2021,5,17]],"date-time":"2021-05-17T12:19:57Z","timestamp":1621253997000},"page":"3481","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Machine Learning Prediction of Fall Risk in Older Adults Using Timed Up and Go Test Kinematics"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4720-5997","authenticated-orcid":false,"given":"Venous","family":"Roshdibenam","sequence":"first","affiliation":[{"name":"Department of Industrial and Systems Engineering, University of Iowa, Iowa City, IA 52242, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gerald J.","family":"Jogerst","sequence":"additional","affiliation":[{"name":"Department of Family Medicine, University of Iowa, Iowa City, IA 52242, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicholas R.","family":"Butler","sequence":"additional","affiliation":[{"name":"Department of Family Medicine, University of Iowa, Iowa City, IA 52242, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4758-4539","authenticated-orcid":false,"given":"Stephen","family":"Baek","sequence":"additional","affiliation":[{"name":"Department of Industrial and Systems Engineering, University of Iowa, Iowa City, IA 52242, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,17]]},"reference":[{"key":"ref_1","unstructured":"(2021, March 10). 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