212 Copyright © 2013, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 10 Neuza Nunes PLUX – Wireless Biosignals S.A., Portugal Diliana Rebelo FCT-UNL, Portugal Rodolfo Abreu FCT-UNL, Portugal Hugo Gamboa FCT-UNL, Portugal Clustering Algorithm for Human Behavior Recognition Based on Biosignal Analysis 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 compari- son with a state-of-the-art algorithm that uses distances between data’s cepstral coefcients to cluster the same biosignals. They are able to successfully replicate the cepstral coefcients 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 diferent activities in it, and achieve an accuracy of 96.9%. Finally, they perform a noise im- munity 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 confrmed the high accuracy level of the developed clustering algorithm for biosignals that express human behavior. Ana Fred IST-UTL, Portugal & Instituto de Telecomunicações, Portugal DOI: 10.4018/978-1-4666-3682-8.ch010