{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T12:54:36Z","timestamp":1789476876964,"version":"build-2803163510"},"reference-count":30,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T00:00:00Z","timestamp":1674086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the project IntelligentCare\u2014Intelligent Multimorbidity Management System","award":["LISBOA-01-0247-FEDER-045948"],"award-info":[{"award-number":["LISBOA-01-0247-FEDER-045948"]}]},{"name":"the project IntelligentCare\u2014Intelligent Multimorbidity Management System","award":["UIDB\/50009\/2020"],"award-info":[{"award-number":["UIDB\/50009\/2020"]}]},{"name":"ERDF\u2014European Regional Development Fund","award":["LISBOA-01-0247-FEDER-045948"],"award-info":[{"award-number":["LISBOA-01-0247-FEDER-045948"]}]},{"name":"ERDF\u2014European Regional Development Fund","award":["UIDB\/50009\/2020"],"award-info":[{"award-number":["UIDB\/50009\/2020"]}]},{"name":"CMU Portugal and the FCT LARSyS\u2014Plurianual funding 2020\u20132023","award":["LISBOA-01-0247-FEDER-045948"],"award-info":[{"award-number":["LISBOA-01-0247-FEDER-045948"]}]},{"name":"CMU Portugal and the FCT LARSyS\u2014Plurianual funding 2020\u20132023","award":["UIDB\/50009\/2020"],"award-info":[{"award-number":["UIDB\/50009\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>More than 37 million falls that require medical attention occur every year, mainly affecting the elderly. Besides the natural consequences of falls, most aged adults with a history of falling are likely to develop a fear of falling, leading to a decrease in their mobility level and impacting their overall quality of life. Previous wrist-based datasets revealed limitations such as unrealistic recording set-ups, lack of proper documentation and, most importantly, the absence of elderly people\u2019s movements. Therefore, this work proposes a new wrist-based dataset to tackle this problem. With this dataset, exhaustive research is carried out with the low computational FS-1 feature set (maximum, minimum, mean and variance) with various machine learning methods. This work presents an accelerometer-only fall detector streaming data at 50 Hz, using the low computational FS-1 feature set to train a 3NN algorithm with Euclidean distance, with a window size of 9 s. This work had battery and memory limitations in mind. It also developed a learning version that boosts the fall detector\u2019s performance over time, achieving no single false positives or false negatives over four days.<\/jats:p>","DOI":"10.3390\/s23031146","type":"journal-article","created":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T04:19:40Z","timestamp":1674101980000},"page":"1146","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Online Fall Detection Using Wrist Devices"],"prefix":"10.3390","volume":"23","author":[{"given":"Jo\u00e3o","family":"Marques","sequence":"first","affiliation":[{"name":"Instituto Superior T\u00e9cnico, Unviersidade de Lisboa, 1049-001 Lisboa, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0496-2050","authenticated-orcid":false,"given":"Plinio","family":"Moreno","sequence":"additional","affiliation":[{"name":"Instituto Superior T\u00e9cnico, Unviersidade de Lisboa, 1049-001 Lisboa, Portugal"},{"name":"Institute for Systems and Robotics, 1049-001 Lisboa, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,19]]},"reference":[{"key":"ref_1","unstructured":"WHO (2021, November 28). 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