Helix: Unsupervised Grammar Induction for Structured Activity Recognition

Abstract

The omnipresence of mobile sensors has brought tremendous opportunities to ubiquitous computing systems. In many natural settings, however, their broader applications are hindered by three main challenges: rarity of labels, uncertainty of activity granularities, and the difficulty of multi-dimensional sensor fusion. In this paper, we propose building a grammar to address all these challenges using a language-based approach. The proposed algorithm, called Helix, first generates an initial vocabulary using unlabeled sensor readings, followed by iteratively combining statistically collocated sub-activities across sensor dimensions and grouping similar activities together to discover higher level activities. The experiments using a 20-minute ping-pong game demonstrate favorable results compared to a Hierarchical Hidden Markov Model (HHMM) baseline. Closer investigations to the learned grammar also shows that the learned grammar captures the natural structure of the underlying activities.