Научны(е)й руководител(и)ь : Christophe MARSALA

Со-руководитель : RIFQI Maria, PINNA Andrea

Towards user-independent emotion recognition and implementation on hardware

This thesis investigates physiological-based emotion recognition in a digital game context and the feasibility of implementing the model on an embedded system. The following chanllenges are addressed: the relationship between emotional states and physiological responses in the game context, individual variabilities of the pschophysiological responses and issues of implementation on an embedded system. The major contributions of this thesis are:     Firstly, we construct a multi-modal Database for Affective Gaming (DAG). This database contains multiple measurements concerning objective modalities such as physiological signals and subjective assessments. We presented statistics of the database and run a series of analysis on emotional moment detection and emotion classification, influencing factors of the overall game experience using various machine learning methods. Secondly, we investigate the individual variability and proposed a personalized group-based model by using the clustering techniques based on physiological traits deduced from optimal feature set. We showed that the proposed personalized group-based model performs better than the general model and user-specific model. Thirdly, we implemente the proposed method on an ARM A9 system and showed that the proposed method can meet the requirement of computation time.

Публикации 2015-2018