Fall detection using single-tree complex wavelet transform

Authors:
Ahmet Yazar;Furkan Keskin;B. Ugur Töreyin;A. Enis Çetin
Affiliations:
Bilkent University, TR-06800 Bilkent, Ankara, Turkey;Bilkent University, TR-06800 Bilkent, Ankara, Turkey;Çankaya University, TR-06810 Yenimahalle, Ankara, Turkey;Bilkent University, TR-06800 Bilkent, Ankara, Turkey
Venue:
Pattern Recognition Letters
Year:
2013

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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.