{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:21:12Z","timestamp":1760710872449,"version":"build-2065373602"},"reference-count":28,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,23]],"date-time":"2022-02-23T00:00:00Z","timestamp":1645574400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council","doi-asserted-by":"publisher","award":["RGPIN-2019-04106"],"award-info":[{"award-number":["RGPIN-2019-04106"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The 6-min walk test (6MWT) is commonly used to assess a person\u2019s physical mobility and aerobic capacity. However, richer knowledge can be extracted from movement assessments using artificial intelligence (AI) models, such as fall risk status. The 2-min walk test (2MWT) is an alternate assessment for people with reduced mobility who cannot complete the full 6MWT, including some people with lower limb amputations; therefore, this research investigated automated foot strike (FS) detection and fall risk classification using data from a 2MWT. A long short-term memory (LSTM) model was used for automated foot strike detection using retrospective data (n = 80) collected with the Ottawa Hospital Rehabilitation Centre (TOHRC) Walk Test app during a 6-min walk test (6MWT). To identify FS, an LSTM was trained on the entire six minutes of data, then re-trained on the first two minutes of data. The validation set for both models was ground truth FS labels from the first two minutes of data. FS identification with the 6-min model had 99.2% accuracy, 91.7% sensitivity, 99.4% specificity, and 82.7% precision. The 2-min model achieved 98.0% accuracy, 65.0% sensitivity, 99.1% specificity, and 68.6% precision. To classify fall risk, a random forest model was trained on step-based features calculated using manually labeled FS and automated FS identified from the first two minutes of data. Automated FS from the first two minutes of data correctly classified fall risk for 61 of 80 (76.3%) participants; however, &lt;50% of participants who fell within the past six months were correctly classified. This research evaluated a novel method for automated foot strike identification in lower limb amputee populations that can be applied to both 6MWT and 2MWT data to calculate stride parameters. Features calculated using automated FS from two minutes of data could not sufficiently classify fall risk in lower limb amputees.<\/jats:p>","DOI":"10.3390\/s22051749","type":"journal-article","created":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T00:53:26Z","timestamp":1645664006000},"page":"1749","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Amputee Fall Risk Classification Using Machine Learning and Smartphone Sensor Data from 2-Minute and 6-Minute Walk Tests"],"prefix":"10.3390","volume":"22","author":[{"given":"Pascale","family":"Juneau","sequence":"first","affiliation":[{"name":"Ottawa Hospital Research Institute, Ottawa, ON K1Y 4E9, Canada"},{"name":"Department of Mechanical Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7025-7501","authenticated-orcid":false,"given":"Natalie","family":"Baddour","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada"}]},{"given":"Helena","family":"Burger","sequence":"additional","affiliation":[{"name":"University Rehabilitation Institute, University of Ljubljana, 1000 Ljubljana, Slovenia"},{"name":"Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia"}]},{"given":"Andrej","family":"Bavec","sequence":"additional","affiliation":[{"name":"University Rehabilitation Institute, University of Ljubljana, 1000 Ljubljana, Slovenia"},{"name":"Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4693-2623","authenticated-orcid":false,"given":"Edward D.","family":"Lemaire","sequence":"additional","affiliation":[{"name":"Ottawa Hospital Research Institute, Ottawa, ON K1Y 4E9, Canada"},{"name":"Faculty of Medicine, University of Ottawa, Ottawa, ON K1N 6N5, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,23]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"A Simple Field Test for The Assessment of Physical Fitness","volume":"53","author":"Balke","year":"1963","journal-title":"Rep. 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