{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T22:23:03Z","timestamp":1775859783558,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,4,17]],"date-time":"2018-04-17T00:00:00Z","timestamp":1523923200000},"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>In order to overcome the current limitations in current threshold-based and machine learning-based fall detectors, an insole system and novel fall classification model were created. Because high-acceleration activities have a high risk for falls, and because of the potential damage that is associated with falls during high-acceleration activities, four low-acceleration activities, four high-acceleration activities, and eight types of high-acceleration falls were performed by twenty young male subjects. Encompassing a total of 800 falls and 320 min of activities of daily life (ADLs), the created Support Vector Machine model\u2019s Leave-One-Out cross-validation provides a fall detection sensitivity (0.996), specificity (1.000), and accuracy (0.999). These classification results are similar or superior to other fall detection models in the literature, while also including high-acceleration ADLs to challenge the classification model, and simultaneously reducing the burden that is associated with wearable sensors and increasing user comfort by inserting the insole system into the shoe.<\/jats:p>","DOI":"10.3390\/s18041227","type":"journal-article","created":{"date-parts":[[2018,4,18]],"date-time":"2018-04-18T03:51:13Z","timestamp":1524023473000},"page":"1227","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["A Novel Detection Model and Its Optimal Features to Classify Falls from Low- and High-Acceleration Activities of Daily Life Using an Insole Sensor System"],"prefix":"10.3390","volume":"18","author":[{"given":"Benjamin","family":"Cates","sequence":"first","affiliation":[{"name":"Department of Bio-Mechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi 16419, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taeyong","family":"Sim","sequence":"additional","affiliation":[{"name":"Department of Bio-Mechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi 16419, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyun Mu","family":"Heo","sequence":"additional","affiliation":[{"name":"Department of Bio-Mechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi 16419, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bori","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Research and Development, Biomaterial Team, Medical Device Development Center, KBIO HEALTH, 123 Osongsaengmyung-ro, Osong-eub, Heungdeok-gu, Cheongju, Chungbuk 28160, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyunggun","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Bio-Mechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi 16419, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joung Hwan","family":"Mun","sequence":"additional","affiliation":[{"name":"Department of Bio-Mechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi 16419, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,4,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.gaitpost.2006.09.012","article-title":"Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm","volume":"26","author":"Bourke","year":"2007","journal-title":"Gait Posture"},{"key":"ref_2","unstructured":"(2017, April 09). 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