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Clustering Algorithm for Human Behavior Recognition Based on Biosignal Analysis

Clustering Algorithm for Human Behavior Recognition Based on Biosignal Analysis

Neuza Nunes, Diliana Rebelo, Rodolfo Abreu, Hugo Gamboa, Ana Fred
Copyright: © 2013 |Pages: 13
ISBN13: 9781466636828|ISBN10: 1466636823|EISBN13: 9781466636835
DOI: 10.4018/978-1-4666-3682-8.ch010
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MLA

Nunes, Neuza, et al. "Clustering Algorithm for Human Behavior Recognition Based on Biosignal Analysis." Human Behavior Recognition Technologies: Intelligent Applications for Monitoring and Security, edited by Hans W. Guesgen and Stephen Marsland, IGI Global, 2013, pp. 212-224. https://doi.org/10.4018/978-1-4666-3682-8.ch010

APA

Nunes, N., Rebelo, D., Abreu, R., Gamboa, H., & Fred, A. (2013). Clustering Algorithm for Human Behavior Recognition Based on Biosignal Analysis. In H. Guesgen & S. Marsland (Eds.), Human Behavior Recognition Technologies: Intelligent Applications for Monitoring and Security (pp. 212-224). IGI Global. https://doi.org/10.4018/978-1-4666-3682-8.ch010

Chicago

Nunes, Neuza, et al. "Clustering Algorithm for Human Behavior Recognition Based on Biosignal Analysis." In Human Behavior Recognition Technologies: Intelligent Applications for Monitoring and Security, edited by Hans W. Guesgen and Stephen Marsland, 212-224. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-3682-8.ch010

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Abstract

Time series unsupervised clustering is accurate in various domains, and there is an increased interest in time series clustering algorithms for human behavior recognition. The authors have developed an algorithm for biosignals clustering, which captures the general morphology of a signal’s cycles in one mean wave. In this chapter, they further validate and consolidate it and make a quantitative comparison with a state-of-the-art algorithm that uses distances between data’s cepstral coefficients to cluster the same biosignals. They are able to successfully replicate the cepstral coefficients algorithm, and the comparison showed that the mean wave approach is more accurate for the type of signals analyzed, having a 19% higher accuracy value. They authors also test the mean wave algorithm with biosignals with three different activities in it, and achieve an accuracy of 96.9%. Finally, they perform a noise immunity test with a synthetic signal and notice that the algorithm remains stable for signal-to-noise ratios higher than 2, only decreasing its accuracy with noise of amplitude equal to the signal. The necessary validation tests performed in this study confirmed the high accuracy level of the developed clustering algorithm for biosignals that express human behavior.

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