Perception and Generation of Affective Hand Movements

Perception and generation of affective movements are essential for achieving the expressivity required for a fully engaging human-machine interaction. This paper develops a computational model for recognizing and generating affective hand movements for display on anthropomorphic and non-anthropomorphic structures. First, time-series features of these movements are aligned and converted to fixed-length vectors using piece-wise linear re-sampling. Next, a feature transformation best capable of discriminating between the affective movements is obtained using functional principal component analysis (FPCA). The resulting low-dimensional feature transformation is used for classification and regeneration. A dataset consisting of one movement type, closing and opening the hand, is considered for this study. Three different expressions, sadness, happiness and anger, were conveyed by a demonstrator through the same general movement. The performance of the developed model is evaluated objectively using leave-one-out cross validation and subjectively through a user study, where participants evaluated the regenerated affective movements as well as the original affective movements reproduced both on a human-like model and a non-anthropomorphic structure. The proposed approach achieves zero leave-one-out cross validation errors, on both the training and testing sets. No significant difference is observed between participants’ evaluation of the regenerated movements as compared to the original movement, which confirms successful regeneration of the affective movement. Furthermore, a significant effect of structure on the perception of affective movements is observed.