title: A wavelet-based approach to fall detection.
abstract: 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.
authors.affiliation: Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, 40136 Bologna, Italy. luca.palmerini@unibo.it.
authors.forename: Luca
authors.initials: L
authors.lastname: Palmerini
authors.affiliation: Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, 40136 Bologna, Italy. fabio.bagala@unibo.it.
authors.forename: Fabio
authors.initials: F
authors.lastname: Bagalà
authors.affiliation: Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, 40136 Bologna, Italy. andrea.zanetti@eggtronic.com.
authors.forename: Andrea
authors.initials: A
authors.lastname: Zanetti
authors.affiliation: Department of Clinical Gerontology, Robert-Bosch-Hospital, 70376 Stuttgart, Germany. Jochen.Klenk@rbk.de.
authors.forename: Jochen
authors.initials: J
authors.lastname: Klenk
authors.affiliation: Department of Clinical Gerontology, Robert-Bosch-Hospital, 70376 Stuttgart, Germany. Clemens.Becker@rbk.de.
authors.forename: Clemens
authors.initials: C
authors.lastname: Becker
authors.affiliation: Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, 40136 Bologna, Italy. angelo.cappello@unibo.it.
authors.forename: Angelo
authors.initials: A
authors.lastname: Cappello
mesh_terms: D061725:Accelerometry / Q000379:methods*; D000058:Accidental Falls; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D005260:Female; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D012372:ROC Curve; D058067:Wavelet Analysis
keywords: accelerometers; fall detection; pattern recognition; wavelet