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Proceedings Paper

Optimization and evaluation of the human fall detection system
Author(s): Hadeel Alzoubi; Naeem Ramzan; Hasan Shahriar; Raid Alzubi; Ryan Gibson; Abbes Amira
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Paper Abstract

Falls are the most critical health problem for elderly people, which are often, cause significant injuries. To tackle a serious risk that made by the fall, we develop an automatic wearable fall detection system utilizing two devices (mobile phone and wireless sensor) based on three axes accelerometer signals. The goal of this study is to find an effective machine learning method that distinguish falls from activities of daily living (ADL) using only a single triaxial accelerometer. In addition, comparing the performance results for wearable sensor and mobile device data .The proposed model detects the fall by using seven different classifiers and the significant performance is demonstrated using accuracy, recall, precision and F-measure. Our model obtained accuracy over 99% on wearable device data and over 97% on mobile phone data.

Paper Details

Date Published: 26 October 2016
PDF: 12 pages
Proc. SPIE 10008, Remote Sensing Technologies and Applications in Urban Environments, 1000816 (26 October 2016); doi: 10.1117/12.2242162
Show Author Affiliations
Hadeel Alzoubi, Univ. of the West of Scotland (United Kingdom)
Naeem Ramzan, Univ. of the West of Scotland (United Kingdom)
Hasan Shahriar, Univ. of the West of Scotland (United Kingdom)
Raid Alzubi, Univ. of the West of Scotland (United Kingdom)
Ryan Gibson, Glasgow Caledonian Univ. (United Kingdom)
Abbes Amira, Univ. of the West of Scotland (United Kingdom)


Published in SPIE Proceedings Vol. 10008:
Remote Sensing Technologies and Applications in Urban Environments
Thilo Erbertseder; Thomas Esch; Nektarios Chrysoulakis, Editor(s)

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