{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T08:43:34Z","timestamp":1765356214061,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2017,6,16]],"date-time":"2017-06-16T00:00:00Z","timestamp":1497571200000},"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>Falls are one of the main health risks among the elderly. A fall detection system based on inertial sensors can automatically detect fall event and alert a caregiver for immediate assistance, so as to reduce injuries causing by falls. Nevertheless, most inertial sensor-based fall detection technologies have focused on the accuracy of detection while neglecting quantization noise caused by inertial sensor. In this paper, an activity model based on tri-axial acceleration and gyroscope is proposed, and the difference between activities of daily living (ADLs) and falls is analyzed. Meanwhile, a Kalman filter is proposed to preprocess the raw data so as to reduce noise. A sliding window and Bayes network classifier are introduced to develop a wearable fall detection system, which is composed of a wearable motion sensor and a smart phone. The experiment shows that the proposed system distinguishes simulated falls from ADLs with a high accuracy of 95.67%, while sensitivity and specificity are 99.0% and 95.0%, respectively. Furthermore, the smart phone can issue an alarm to caregivers so as to provide timely and accurate help for the elderly, as soon as the system detects a fall.<\/jats:p>","DOI":"10.3390\/s17061393","type":"journal-article","created":{"date-parts":[[2017,6,16]],"date-time":"2017-06-16T10:06:31Z","timestamp":1497607591000},"page":"1393","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["An Unobtrusive Fall Detection and Alerting System Based on Kalman Filter and Bayes Network Classifier"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1504-0657","authenticated-orcid":false,"given":"Jian","family":"He","sequence":"first","affiliation":[{"name":"Beijing Advanced Innovation Center for Future Internet Technology, Beijing 100124, China"},{"name":"Beijing Engineering Research Center for IoT Software and Systems, Beijing 100124, China"},{"name":"School of Software Engineering, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuang","family":"Bai","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyi","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Advanced Innovation Center for Future Internet Technology, Beijing 100124, China"},{"name":"Beijing Engineering Research Center for IoT Software and Systems, Beijing 100124, China"},{"name":"School of Software Engineering, Beijing University of Technology, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,6,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"324","DOI":"10.2522\/ptj.20070107","article-title":"Falls in the Medicare Population: Incidence, Associated Factors, and Impact on Health Care","volume":"89","author":"Ciol","year":"2009","journal-title":"Phys. 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