Towards Real-Time Affect Detection Based on Sample Entropy Analysis of Expressive Gesture

Aiming at providing a solid foundation to the creation of future affect detection applications in HCI, we propose to analyze human expressive gesture by computing movement Sample Entropy (SampEn). This method provides two main advantages: (i) it is adapted to the non-linearity and non-stationarity of human movement; (ii) it allows a fine-grain analysis of the information encoded in the movement features dynamics. A realtime application is presented, implementing the SampEn method. Preliminary results obtained by computing SampEn on two expressive features, smoothness and symmetry, are provided in a video available on the web.