A comparison of unsupervised learning algorithms for gesture clustering

Abstract

Gesture recognition is an important aspect of interpersonal social interaction. Developing a similar capacity in a robot will improve human-robot interaction. Various unsupervised clustering methods applied to clustering a set of dynamic human arm gestures are compared. Unsupervised clustering is important in gesture recognition as it imposes no a priori bound on the set of gestures. Results are compared using v-measure, a metric that allows differential weighting between clustering homogeneity and completeness. Experiments show that the best clustering method depends on the desired balance between homogeneity and completeness.