Recognition of Human Movement Patterns during a Human-Agent Interaction
Ronald Böck · 2018
The analysis of human behaviour during an interaction with an interlocutor shows a large number of facets. An interesting part is the moving behaviour of a subject while communicating, especially in the context of a human-computer interaction. In particular, the paper focusses on the recognition of movement patterns in a room during an interaction with two virtual agents. For this, we investigated a close-to-real-life scenario providing two virtual agents on different screens (i.e. the CASIA Coffee House Corpus). In this context, a feature set consisting of ten statistical features for the (automatic) recognition of movement patterns is proposed. Further, we automatically clustered the samples provided in the corpus and cross-checked the results with a manual annotation. For this, we identified two meaningful movement patterns for which we assume that they will appear also in similar other scenarios. Finally, we automatically classified the movement patterns based on the proposed features applying a multi-layer perceptron. We obtained an average error rate of 12.0%.