Towards Recognizing Abstract Activities: An Unsupervised Approach

The recognition of abstract high-level activities using wearable sensors is an important prerequisite for context aware mobile assistance, especially in AAL and medical care applications. A major difficulty in detecting this type of activities is that different activities often share similar motion patterns. One possible solution is to aggregate these activities from shorter, easier to detect base level actions, but the explicit annotation of these is not trivial and very time consuming. In this paper we introduce a simple clustering based method for the recognition of compound activities at a high level of abstraction using k-Means as an unsupervised learning algorithm. A general problem of these methods is that the resulting cluster affiliations are typically not human readable and some kind of interpretation is needed. To achieve this, we developed a hybrid approach using a generative probabilistic model built on top of the clusterer. We adapted a Hidden Markov Model for mapping the cluster memberships onto high-level activities and sucessfully evaluated the feasibility of this technique using experimental data from two test runs of a home care scenario showing a higher accuracy and robustness than conventional discriminative methods.