This work investigates classification of emotions from MoCap full-body data by using Convolutional Neural Networks (CNN). Rather than addressing regular day to day activities, we focus on a more complex type of full-body movement - dance. For this purpose, a new dataset was created which contains short excerpts of the performances of professional dancers who interpreted four emotional states: anger, happiness, sadness, and insecurity. Fourteen minutes of motion capture data are used to explore different CNN architectures and data representations. The results of the four-class classification task are up to 0.79 (F1 score) on test data of other performances by the same dancers. Hence, through deep learning, this paper proposes a novel and effective method of emotion classification which can be exploited in affective interfaces.

From motions to emotions: classification of affect from dance movements using deep learning / Karumuri, Sukumar; Volpe, Gualtiero; Niewiadomski, Radoslaw; Camurri, Antonio. - ELETTRONICO. - (2019), pp. 1-6. ((Intervento presentato al convegno 2019 CHI Conference on Human Factors in Computing Systems, CHI EA 2019 tenutosi a Glasgow nel 04/05/2019 [10.1145/3290607.3312910].

From motions to emotions: classification of affect from dance movements using deep learning

Radoslaw Niewiadomski;
2019

Abstract

This work investigates classification of emotions from MoCap full-body data by using Convolutional Neural Networks (CNN). Rather than addressing regular day to day activities, we focus on a more complex type of full-body movement - dance. For this purpose, a new dataset was created which contains short excerpts of the performances of professional dancers who interpreted four emotional states: anger, happiness, sadness, and insecurity. Fourteen minutes of motion capture data are used to explore different CNN architectures and data representations. The results of the four-class classification task are up to 0.79 (F1 score) on test data of other performances by the same dancers. Hence, through deep learning, this paper proposes a novel and effective method of emotion classification which can be exploited in affective interfaces.
Conference on Human Factors in Computing Systems - Proceedings
USA
Association for Computing Machinery
9781450359719
From motions to emotions: classification of affect from dance movements using deep learning / Karumuri, Sukumar; Volpe, Gualtiero; Niewiadomski, Radoslaw; Camurri, Antonio. - ELETTRONICO. - (2019), pp. 1-6. ((Intervento presentato al convegno 2019 CHI Conference on Human Factors in Computing Systems, CHI EA 2019 tenutosi a Glasgow nel 04/05/2019 [10.1145/3290607.3312910].
Karumuri, Sukumar; Volpe, Gualtiero; Niewiadomski, Radoslaw; Camurri, Antonio
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/278950
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