  Traditional activity recognition systems work on the basis of training,
taking a fixed set of sensors into account. In this article, we focus on the
question how pattern recognition can leverage new information sources without
any, or with minimal user input. Thus, we present an approach for opportunistic
activity recognition, where ubiquitous sensors lead to dynamically changing
input spaces. Our method is a variation of well-established principles of
machine learning, relying on unsupervised clustering to discover structure in
data and inferring cluster labels from a small number of labeled dates in a
semi-supervised manner. Elaborating the challenges, evaluations of over 3000
sensor combinations from three multi-user experiments are presented in detail
and show the potential benefit of our approach.
