Towards a deep learning-based activity discovery system

Activity discovery is a challenging machine learning problem where we seek to uncover new or altered behavioural patterns in sensor data. In this paper we motivate and introduce a novel approach to activity discovery based on modern deep learning techniques. We hypothesise that our proposed approach can deal with interleaved datasets in a more intelligent manner than most existing AD methods. We also build upon prior work building hierarchies of activities that capture the inherent aggregate nature of complex activities and show how this could plausibly be adapted to work with the deep learning technique we present. Finally, we briefly talk about the challenge of evaluating activity discovery systems in a fair way and outline our future plans for implementing this model.