A Model-Based Human Activity Recognition for Human-Robot Collaboration

Abstract

Human activity recognition is a crucial ingredient in safe and efficient human–robot collaboration. In this paper, we present a new model-based human activity recognition system called logical activity recognition system (LCARS). LCARS requires much less training data compared to learning-based works. Compared to other model-based works, LCARS requires minimal domain-specific modeling effort from users. The minimal modeling is for two reasons: i) we provide a systematic and intuitive way to encode domain knowledge for LCARS and ii) LCARS automatically constructs a probabilistic estimation model from the domain knowledge. Requiring minimal training data and modeling effort allows LCARS to be easily applicable to various scenarios. We verify this through simulations and experiments.