On gestures and postural behavior as a modality in ensemble methods

Abstract

Knowledge about the users emotional state is important to achieve human like, natural HCI in modern technical systems. Humans rely on body gestures and posture when communicating. We investigate the relation between gestures and human emotion, specifically when completing tasks. The main focus of this work lies on discriminating between mental overload and mental underload, which can e.g. be useful in an e-tutorial system. Mental underload is a new term used to describe the state a person is in when completing a dull or boring task. It will be shown how to select suited features, such as gestures, movement and postural behavior. Furthermore those features will be investigated regarding their discriminative power. After features are selected, a multiple classifier system will be designed, trained and evaluated.