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Sensors 2017, 17(7), 1487; doi:10.3390/s17071487

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

1
Department of Computer Engineering, Yildiz Technical University, 34220 Istanbul, Turkey
2
IT Department, Garanti Technology, 34212 Istanbul, Turkey
3
Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey, GU2 7XH Guildford, UK
*
Author to whom correspondence should be addressed.
Received: 11 April 2017 / Revised: 12 June 2017 / Accepted: 17 June 2017 / Published: 23 June 2017
(This article belongs to the Section Sensor Networks)
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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. View Full-Text
Keywords: activity classification; fall detection; multi-tier architecture; energy efficient smartphone application; simple thresholding; machine learning activity classification; fall detection; multi-tier architecture; energy efficient smartphone application; simple thresholding; machine learning
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MDPI and ACS Style

Guvensan, M.A.; Kansiz, A.O.; Camgoz, N.C.; Turkmen, H.I.; Yavuz, A.G.; Karsligil, M.E. An Energy-Efficient Multi-Tier Architecture for Fall Detection on Smartphones. Sensors 2017, 17, 1487.

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