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Sensors 2015, 15(8), 17827-17894; doi:10.3390/s150817827

Analysis of Android Device-Based Solutions for Fall Detection

Departamento de Tecnología Electrónica, ETSI Telecomunicación, Universidad de Málaga, 29071 Málaga, Spain
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Author to whom correspondence should be addressed.
Academic Editor: Ki H. Chon
Received: 15 June 2015 / Revised: 14 July 2015 / Accepted: 17 July 2015 / Published: 23 July 2015
(This article belongs to the Special Issue Smartphone-Based Sensors for Non-Invasive Physiological Monitoring)
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Abstract

Falls are a major cause of health and psychological problems as well as hospitalization costs among older adults. Thus, the investigation on automatic Fall Detection Systems (FDSs) has received special attention from the research community during the last decade. In this area, the widespread popularity, decreasing price, computing capabilities, built-in sensors and multiplicity of wireless interfaces of Android-based devices (especially smartphones) have fostered the adoption of this technology to deploy wearable and inexpensive architectures for fall detection. This paper presents a critical and thorough analysis of those existing fall detection systems that are based on Android devices. The review systematically classifies and compares the proposals of the literature taking into account different criteria such as the system architecture, the employed sensors, the detection algorithm or the response in case of a fall alarms. The study emphasizes the analysis of the evaluation methods that are employed to assess the effectiveness of the detection process. The review reveals the complete lack of a reference framework to validate and compare the proposals. In addition, the study also shows that most research works do not evaluate the actual applicability of the Android devices (with limited battery and computing resources) to fall detection solutions. View Full-Text
Keywords: fall detection; smartphone; eHealth; Android; accelerometer fall detection; smartphone; eHealth; Android; accelerometer
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Casilari, E.; Luque, R.; Morón, M.-J. Analysis of Android Device-Based Solutions for Fall Detection. Sensors 2015, 15, 17827-17894.

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