Helix: Unsupervised Grammar Induction for Structured Activity Recognition
Huan-Kai Peng, Pang Wu, Jiang Zhu, Joy Ying Zhang · 2011
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.