Emotion Recognition for Exergames using Laban Movement Analysis

Exergames do not have the capacity to detect whether the players are really enjoying the game-play. The games are not intelligent enough to detect significant emotional states and adapt accordingly in order to offer a better user experience for the players. We propose a set of body motion features, based on the Effort component of Laban Movement Analysis (LMA), that are used to provide sets of classifiers for emotion recognition in a game scenario for four emotional states:concentration, meditation, excitement and frustration. Experimental results show that, the system is capable of successfully recognizing the four different emotional states at a very high rate.