文献类型：期刊文献

英文题名：Development of a Real-Time Wearable Fall Detection System in the Context of Internet of Things

作者：Qian, Zhiqin[1,2];Lin, Yuchen[1,2];Jing, Weiji[1,2];Ma, Zhekai[3];Liu, Hao[4];Yin, Ruixue[1,2];Li, Zezhi[5];Bi, Zhuming[6];Zhang, Wenjun[7]

机构：[1]East China Univ Sci & Technol, Complex & Intelligent Syst Res Lab, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai 200237, Peoples R China;[3]Shanghai Fudan Microelect Grp Co Ltd, Shanghai, Peoples R China;[4]SAIC Volkswagen Co Ltd, Shanghai, Peoples R China;[5]Ren Ji Hosp, Dept Neurol, Shanghai 200002, Peoples R China;[6]Purdue Univ Ft Wayne, Dept Engn, Ft Wayne, IN 46805 USA;[7]Univ Saskatchewan, Dept Mech Engn, Saskatoon, SK S7N 5A2, Canada

年份：2022

卷号：9

期号：21

起止页码：21999

外文期刊名：IEEE INTERNET OF THINGS JOURNAL

收录：;EI(收录号：20222612275564);WOS:【SCI-EXPANDED(收录号:WOS:000871080800097)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 51375166, and in part by the Science and Technology Industry Research Institute of Minhang District under Grant 2019MHC066.

语种：英文

外文关键词：Fall detection; Monitoring; Cloud computing; Servers; Sensors; Internet of Things; Wearable computers; Fall detection; multilevel threshold; narrow band Internet of Things (NB-IoT); wearable devices

摘要：Fall detection is of increasing significance in terms of the health monitoring of the elderly and disabled people, as falls may lead to physical injuries or even mental trauma. The existing fall detection methods have achieved impressive performance, but limitations present in operability, interface with public healthcare systems, and other technical issues such as high-power consumption, cost, and reliability. In this article, we present a wearable fall detection system, which is based on a novel multilevel threshold algorithm. The algorithm combines micro-electro-mechanical-systems (MEMS) with narrow band Internet of Things (NB-IoT). The system also includes a user interface for healthcare professionals, developed based on the cloud technology and server-client architecture. For the verification of the algorithm, we recruited 20 volunteers to perform the activities of daily living and falls. The experimental result showed that the proposed algorithm can achieve an accuracy of 94.88%, a sensitivity of 95.25%, and a specificity of 94.5%, suggesting the effectiveness of our system.