Multimodal Recognition of Emotions Using Physiological Signals with the Method of Decision-Level Fusion for Healthcare Applications

Chaka Koné (1) , Imen Tayari-Meftah (2) , Nhan Le Thanh (2) , Cecile Belleudy (1)

1 LEAT - Laboratoire d'Electronique, Antennes et Télécommunications
2 I3S - Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis

Automatic emotion recognition enhance dramatically the development of human/machine dialogue. Indeed, it allows computers to determine the emotion felt by the user and adapt consequently its behavior. This paper presents a new method for the fusion of signals for the purpose of a multimodal recognition of eight basic emotions using physiological signals. After a learning phase where an emotion data base is constructed, we apply the recognition algorithm on each modality separately. Then, we merge all these decisions separately by applying a decision fusion approach to improve recognition rate. The experiments show that the proposed method allows high accuracy emotion recognition. Indeed we get a recognition rate of 81.69% under some conditions.

Mots clés: Signal fusion method Basic emotions Multimodal detection Physiological signals; Signal fusion method Basic emotions Multimodal detection Physiological signals

Domaines: Informatique [cs]; Systèmes embarqués; Apprentissage [cs.LG]; Traitement du signal et de l'image [eess.SP]; Electronique

Chaka Koné, Imen Tayari-Meftah, Nhan Le Thanh, Cecile Belleudy. Multimodal Recognition of Emotions Using Physiological Signals with the Method of Decision-Level Fusion for Healthcare Applications. Computer Science. Lecture Notes in Computer Science, 9102, Springer, Cham, pp.301-306, 2015, Inclusive Smart Cities and e-Health, 978-3-319-19312-0. ⟨10.1007/978-3-319-19312-0_26⟩. ⟨hal-01516225⟩