Abstract
Human activity recognition using embedded mobile and embedded sensors is becoming increasingly important. Scaling up from individuals to groups, that is, Group Activity Recognition (GAR), has attracted significant attention recently. This article proposes a model and modeling language for GAR called GroupSense-L and a novel distributed middleware called GroupSense for mobile GAR. We implemented and tested GroupSense using smartphone sensors, smartwatch sensors, and embedded sensors in things, where we have a protocol for these different devices to exchange information required for GAR. A range of continuous group activities (from simple to fairly complex) illustrates our approach and demonstrates the feasibility of our model and richness of the proposed specialization. We then conclude with lessons learned for GAR and future work.
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