Fall detection using single-tree complex wavelet transform
Date
2013Source Title
Pattern Recognition Letters
Print ISSN
0167-8655
Publisher
Elsevier
Volume
34
Issue
15
Pages
1945 - 1952
Language
English
Type
ArticleItem Usage Stats
287
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267
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Abstract
The goal of Ambient Assisted Living (AAL) research is to improve the quality of life of the elderly and handicapped people and help them maintain an independent lifestyle with the use of sensors, signal processing and telecommunications infrastructure. Unusual human activity detection such as fall detection has important applications. In this paper, a fall detection algorithm for a low cost AAL system using vibration and passive infrared (PIR) sensors is proposed. The single-tree complex wavelet transform (ST-CWT) is used for feature extraction from vibration sensor signal. The proposed feature extraction scheme is compared to discrete Fourier transform and mel-frequency cepstrum coefficients based feature extraction methods. Vibration signal features are classified into "fall" and "ordinary activity" classes using Euclidean distance, Mahalanobis distance, and support vector machine (SVM) classifiers, and they are compared to each other. The PIR sensor is used for the detection of a moving person in a region of interest. The proposed system works in real-time on a standard personal computer.
Keywords
Falling person detectionFeature extraction
PIR sensor
Single-tree complex wavelet transform
Support vector machines
Vibration sensor
Ambient assisted living (AAL)
Complex wavelet transforms
Feature extraction methods
Mel frequency cepstrum coefficients
Person detection
Pir sensors
Telecommunications infrastructures
Vibration sensors
Discrete Fourier transforms
Feature extraction
Image segmentation
Personal computers
Sensors
Support vector machines
Ventilation exhausts
Wavelet transforms
Forestry
Permalink
http://hdl.handle.net/11693/21106Published Version (Please cite this version)
http://dx.doi.org/10.1016/j.patrec.2012.12.010Collections
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