Fall detection using single-tree complex wavelet transform
Pattern Recognition Letters
1945 - 1952
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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.
KeywordsFalling person detection
Single-tree complex wavelet transform
Support vector machines
Ambient assisted living (AAL)
Complex wavelet transforms
Feature extraction methods
Mel frequency cepstrum coefficients
Discrete Fourier transforms
Support vector machines
Published Version (Please cite this version)http://dx.doi.org/10.1016/j.patrec.2012.12.010
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