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Browsing by Author "Yazar, Ahmet"

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    Citation Count: Yazar, A...et al. (2013). Fall detection using single-tree complex wavelet transform. Pattern Recognition Letters, 34(15), 1945-1952. http://dx.doi.org/10.1016/j.patrec.2012.12.010
    Fall detection using single-tree complex wavelet transform
    (Elsevier Science Bv, 2013) Yazar, Ahmet; Keskin, Furkan; Töreyin, Behçet Uğur; Çetin, A. Enis; 19325; 2147
    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.
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    Citation Count: Yazar, Ahmet; Çetin, A. Enis; Töreyin, B. Uǧur (2012). "Human activity classification using vibration and PIR sensors", 2012 20th Signal Processing and Communications Applications Conference, SIU 2012.
    Human activity classification using vibration and PIR sensors
    (2012) Yazar, Ahmet; Çetin, A. Enis; Töreyin, B. Uǧur; 19325
    Fall detection is an important problem for elderly people living independently and people in need of care. In this paper, a fall detection method using seismic and passive infrared (PIR) sensors is proposed. Fast Fourier transform, mel-frequency cepstrum coefficients, and discrete wavelet transform based features are extracted for classification. Seismic signals are classified into "fall" and "not a fall" classes using support vector machines. Once a moving person is detected by the PIR sensor within a region of interest, fall is detected by fusing seismic and PIR sensor decisions. The proposed system is implemented on a standard personal computer and works in real-time. © 2012 IEEE.