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An Energy-Efficient Multi-Tier Architecture for Fall Detection on Smartphones

Guvensan, M. Amac, Kansizand make it available, A. Oguz, Camgöz, Necati Cihan, Turkmen, H. Irem, Yavuz, A. Gokhan and Karsligil, M. Elif (2017) An Energy-Efficient Multi-Tier Architecture for Fall Detection on Smartphones Sensors, 17 (1487).

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Abstract

Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms. The proposed method is implemented on a mobile application, called uSurvive, for Android smartphones. It runs as a background service and monitors the activities of a person in daily life and automatically sends a notification to the appropriate authorities and/or user defined contacts when it detects a fall. The performance of the proposed method was evaluated in terms of fall detection performance and energy consumption. Real life performance tests conducted on two different models of smartphone demonstrate that our 3-tier architecture with feature reduction could save up to 62% of energy compared to machine learning only solutions. In addition to this energy saving, the hybrid method has a 93% of accuracy, which is superior to thresholding methods and better than machine learning only solutions.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
NameEmailORCID
Guvensan, M. AmacUNSPECIFIEDUNSPECIFIED
Kansizand make it available, A. OguzUNSPECIFIEDUNSPECIFIED
Camgöz, Necati Cihann.camgoz@surrey.ac.ukUNSPECIFIED
Turkmen, H. IremUNSPECIFIEDUNSPECIFIED
Yavuz, A. GokhanUNSPECIFIEDUNSPECIFIED
Karsligil, M. ElifUNSPECIFIEDUNSPECIFIED
Date : 23 June 2017
Identification Number : 10.3390/s17071487
Copyright Disclaimer : © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Depositing User : Necati Cihan Camgöz
Date Deposited : 26 Jul 2017 10:54
Last Modified : 26 Jul 2017 10:54
URI: http://epubs.surrey.ac.uk/id/eprint/841736

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