Clustering Algorithm for Human Behavior Recognition Based on Biosignal Analysis

Clustering Algorithm for Human Behavior Recognition Based on Biosignal Analysis

Neuza Nunes (PLUX – Wireless Biosignals S.A., Portugal), Diliana Rebelo (FCT-UNL, Portugal), Rodolfo Abreu (FCT-UNL, Portugal), Hugo Gamboa (FCT-UNL, Portugal) and Ana Fred (IST-UTL, Portugal & Instituto de Telecomunicações, Portugal)
DOI: 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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Nunes (2012) presented an advanced signal processing algorithm for pattern recognition and clustering purposes applied to time varying signals collected from the human body. The recognition of differences in the signal’s morphology produced by physiological abnormalities (arrhythmia, for example) or different conditions of the subject’s state (walking or running, for example) was tested by collecting a set of cyclic biosignals with two distinctive modes. The acquired signals were the input of the generic algorithm. This algorithm knows beforehand the number of modes the signal has and is the base function to identify the individual cycles in a signal. The result of this algorithm is a single wave, a mean wave which is an averaging of all signal cycles aligned in a notable point.

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