Abstract:We study sensor-based human activity recognition in manual work processes like assembly tasks. In such processes, the system states often have a rich structure, involving object properties and relations. Thus, estimating the hidden system state from sensor observations by recursive Bayesian filtering can be very challenging, due to the combinatorial explosion in the number of system states.To alleviate this problem, we propose an efficient Bayesian filtering model for such processes. In our approach, system st… Show more
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