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Human activity recognition is an important functionality in any intelligent system designed to support human daily activities. While majority of human activity recognition systems use supervised learning, these systems lack the ability to detect new activities by themselves. In this paper, we report the results of our investigation of unsupervised human activity detection with features extracted from skeleton data obtained from RGBD sensor. Unlike activity recognition, activity detection does not provide the label however attempts to distinguish one activity from another. This paper demonstrates a suitable set of features to be used with K-means clustering to distinguish different activities from a pool of unlabeled observations. The results show 100% F0.5-score were achieved for six out of nine activities for one of the subjects at low frame rate, while F0.5-score of 71.9% was achieved on average for all activities by four subjects.