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In this work, we recorded accelerometer data from 35 healthy individuals performing various ADLs, as well as falls. Spatial and frequency domain features were extracted and used for the training of machine learning models with the aim of distinguishing between fall and no fall events, as well as between falls and other ADLs. Supervised classification experiments demonstrated the efficiency of the proposed approach, achieving an F1-score of 98.41% for distinguishing between fall and no fall events, and an F1-score of 88.11% for distinguishing between various ADLs, including falls. Furthermore, the created dataset, named \u201cShimFall&amp;ADL\u201d will be publicly released to facilitate further research on the field.<\/jats:p>","DOI":"10.3390\/s20133777","type":"journal-article","created":{"date-parts":[[2020,7,6]],"date-time":"2020-07-06T09:49:11Z","timestamp":1594028951000},"page":"3777","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Triaxial Accelerometer-Based Falls and Activities of Daily Life Detection Using Machine Learning"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6674-7890","authenticated-orcid":false,"given":"Turke","family":"Althobaiti","sequence":"first","affiliation":[{"name":"Rafha Community College, Nothern Border University, Rafha 76413, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stamos","family":"Katsigiannis","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Physical Sciences, University of the West of Scotland, High St., Paisley PA1 2BE, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naeem","family":"Ramzan","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Physical Sciences, University of the West of Scotland, High St., Paisley PA1 2BE, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1109\/TSMC.2016.2617465","article-title":"Multiview Cauchy Estimator Feature Embedding for Depth and Inertial Sensor-Based Human Action Recognition","volume":"47","author":"Guo","year":"2017","journal-title":"IEEE Trans. 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