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Sensors 2014, 14(10), 18543-18574; doi:10.3390/s141018543

Comparison and Characterization of Android-Based Fall Detection Systems

Universidad de Málaga, Departamento de Tecnología Electrónica, ETSI Telecomunicación, 29071 Málaga, Spain
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Received: 25 June 2014 / Revised: 22 September 2014 / Accepted: 23 September 2014 / Published: 8 October 2014
(This article belongs to the Special Issue Wireless Sensor Network for Pervasive Medical Care)
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Abstract

Falls are a foremost source of injuries and hospitalization for seniors. The adoption of automatic fall detection mechanisms can noticeably reduce the response time of the medical staff or caregivers when a fall takes place. Smartphones are being increasingly proposed as wearable, cost-effective and not-intrusive systems for fall detection. The exploitation of smartphones’ potential (and in particular, the Android Operating System) can benefit from the wide implantation, the growing computational capabilities and the diversity of communication interfaces and embedded sensors of these personal devices. After revising the state-of-the-art on this matter, this study develops an experimental testbed to assess the performance of different fall detection algorithms that ground their decisions on the analysis of the inertial data registered by the accelerometer of the smartphone. Results obtained in a real testbed with diverse individuals indicate that the accuracy of the accelerometry-based techniques to identify the falls depends strongly on the fall pattern. The performed tests also show the difficulty to set detection acceleration thresholds that allow achieving a good trade-off between false negatives (falls that remain unnoticed) and false positives (conventional movements that are erroneously classified as falls). In any case, the study of the evolution of the battery drain reveals that the extra power consumption introduced by the Android monitoring applications cannot be neglected when evaluating the autonomy and even the viability of fall detection systems. 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

Luque, R.; Casilari, E.; Morón, M.-J.; Redondo, G. Comparison and Characterization of Android-Based Fall Detection Systems. Sensors 2014, 14, 18543-18574.

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