We present a technique for modeling and recognising human activity from moving light displays using hidden Markovmodels. We extract a small number of joint angles at each frame to form a feature vector. Continuous hidden Markovmodels are then trained with the resulting time series, one for each of a variety of human activity, using the Baum-Welch algorithm. Motion classification is then attempted by evaluation of the forward variable for each model usingpreviously unseen test data. Experimental results based on real-world human motion capture data demonstrate the performanceof the algorithm and some degree of robustness to data noise and human motion irregularity. This techniquehas potential applications in activity classification for gesture-based game interfaces and character animation.
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