Hand Motion Gestural Oscillations Multimodal Discourse

To develop multimodal interfaces, one needs to understand the constraints underlying human communicative gesticulation and the kinds of features one may compute based on these underlying human characteristics.In this paper we address hand motion oscillatory gesture detection in natural speech and conversation. First, the hand motion trajectory signals are extracted from video. Second, a wavelet analysis based approach is presented to process the signals. In this approach, wavelet ridges are extracted from the responses of wavelet analysis for the hand motion trajectory signals, which can be used to characterize frequency properties of the hand motion signals. The hand motion oscillatory gestures can be extracted from these frequency properties. Finally, we relate the hand motion oscillatory gestures to the phases of speech and multimodal discourse analysis. We demonstrate the efficacy of the system on a real discourse dataset in which a subject described her action plan to an interlocutor. We extracted the oscillatory gestures from the x, y and z motion traces of both hands. We further demonstrate the power of gestural oscillation detection as a key to unlock the structure of the underlying discourse.