Incorporating Unsupervised Learning in Activity Recognition

Users are constantly involved in a multitude of activities in ever-changing context. Analyzing activities in contextrich environments has become a great challenge in contextawareness research. Traditional methods for activity recognition, such as classification, cannot cope with the variety and dynamicity of context and activities. In this paper, we propose an activity recognition approach that incorporates unsupervised learning. We analyze the feasibility of applying subspace clustering—a specific type of unsupervised learning— to high-dimensional, heterogeneous sensory input. Then we present the correspondence between clustering output and classification input. This approach has the potential to discover implicit, evolving activities, and can provide valuable assistance to traditional classification based methods. As sensors become prevalent means in context detection and information channels proliferate to make context sharing easier, it is increasingly challenging to interpret context and analyze its effects on the activities (Lim and Dey 2010). We argue that applying traditional approaches to activity recognition may become more and more difficult to apply in context and activity-rich environments. In the literature, context attributes used for learning activities are chosen by either empirical assumption or dimension reduction to render a small set of features (Krause, Smailagic, and Siewiorek 2006). These approaches are infeasible in face of a broad spectrum of context information. The most significant drawback is that they fail to acknowledge the large variety of features needed to describe different activities. For activity recognition, most previous works applied supervised learning approaches that aimed at predicting activities among a set of known classes(Ferscha et al. 2004). These approaches, however, are also challenged when coping with new and fast evolving activities. Unsupervised learning, particularly clustering, has been highly successful for revealing implicit relationships and regularities in large data sets. Intuitively, we can envisage an activity recognition approach that applies clustering to context history. The clusters, representing frequent context patterns, can suggest activities and their contextual conditions. The results can be used independently for analyzing and interpreting activities. Furthermore, clusters can reveal the scopes and conditions  This information is valuable when determining the scopes of information sharing in pervasive environments. Although clustering is a promising approach in discovering associations within context, the feasibility of traditional clustering is questionable in dealing with high dimensionality and heterogeneity of context data. In this paper, we will first conduct a detailed analysis about the challenges to apply clustering for activity recognition. Afterwards we introduce two recent subspace clustering methods that can address these challenges. Lastly, based on the analysis of unsupervised activity recognition, we will propose an activity recognition framework that incorporates clustering in conventional classification. We will show that the two directions are complementary to each other, and developing a hybrid approach will greatly benefit activity context awareness. Unsupervised activity learning