An Incremental Approach of Clustering for Human Activity Discovery

抄録

One of the challenges in human activity recognition is the ability for an intelligent system to discover the activity models by itself. In this paper, we propose an incremental approach to discover human activities from unlabeled data using K-means. The approach does not require prior specification of the number of clusters, or k-value, and has the ability to reject random movements or noise. Simple algorithm is used making the approach easy to implement without requiring any prior knowledge in the data. We evaluated the effectiveness of the approach and the results show more than 30% improvement in precision and 19% improvement in recall when compared to the results obtained using a non-incremental approach with cluster validity index. The achievement in human activity discovery will enable the wide adoption of human activity recognition technologies in the natural human living environment where labeled data are not available.