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Open AccessArticle

A Wavelet-Based Approach to Fall Detection

1
Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, 40136 Bologna, Italy
2
Department of Clinical Gerontology, Robert-Bosch-Hospital, 70376 Stuttgart, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Panicos Kyriacou
Sensors 2015, 15(5), 11575-11586; https://doi.org/10.3390/s150511575
Received: 24 February 2015 / Revised: 29 April 2015 / Accepted: 13 May 2015 / Published: 20 May 2015
(This article belongs to the Collection Sensors for Globalized Healthy Living and Wellbeing)
Falls among older people are a widely documented public health problem. Automatic fall detection has recently gained huge importance because it could allow for the immediate communication of falls to medical assistance. The aim of this work is to present a novel wavelet-based approach to fall detection, focusing on the impact phase and using a dataset of real-world falls. Since recorded falls result in a non-stationary signal, a wavelet transform was chosen to examine fall patterns. The idea is to consider the average fall pattern as the “prototype fall”.In order to detect falls, every acceleration signal can be compared to this prototype through wavelet analysis. The similarity of the recorded signal with the prototype fall is a feature that can be used in order to determine the difference between falls and daily activities. The discriminative ability of this feature is evaluated on real-world data. It outperforms other features that are commonly used in fall detection studies, with an Area Under the Curve of 0.918. This result suggests that the proposed wavelet-based feature is promising and future studies could use this feature (in combination with others considering different fall phases) in order to improve the performance of fall detection algorithms. View Full-Text
Keywords: fall detection; wavelet; accelerometers; pattern recognition fall detection; wavelet; accelerometers; pattern recognition
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Palmerini, L.; Bagalà, F.; Zanetti, A.; Klenk, J.; Becker, C.; Cappello, A. A Wavelet-Based Approach to Fall Detection. Sensors 2015, 15, 11575-11586.

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