Human activity recognition is an important ability in any system that supports human in performing their daily activities. However, current supervised approach in human activity recognition is difficult to be deployed in the natural human living environment where labeled observations are scarce. In this paper, we demonstrate the use of K-means clustering and simple template models to achieve human activity detection and recognition in an unsupervised manner. The features used are extracted from the skeleton data obtained from an inexpensive RGBD (RGB Depth) sensor. Our results show an average detection performance of 80.4% precision and 83.8% recall. The availability of an unsupervised approach in human activity recognition will make possible the wide adoption of human activity recognition in the natural human living environment.