Expressive motions recognition and analysis with learning and statistical methods

This paper proposes to recognize and analyze expressive gestures using a descriptive motion language, the Laban Movement Analysis (LMA) method. We extract body features based on LMA factors which describe both quantitative and qualitative aspects of human movement. In the direction of our study, a dataset of 5 gestures performed with 4 emotions is created using the motion capture Xsens. We used two different approaches for emotions analysis and recognition. The first one is based on a machine learning method, the Random Decision Forest. The second approach is based on the human’s perception. We derive the most important features for each expressed emotion using the same methods, the RDF and the human’s ratings. We compared the results obtained from the automatic learning method against human perception in the discussion section.