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

  • Chaka Koné
  • Imen Meftah Tayari
  • Nhan Le-Thanh
  • Cecile Belleudy
Conference paper

DOI: 10.1007/978-3-319-19312-0_26

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9102)
Cite this paper as:
Koné C., Tayari I.M., Le-Thanh N., Belleudy C. (2015) Multimodal Recognition of Emotions Using Physiological Signals with the Method of Decision-Level Fusion for Healthcare Applications. In: Geissbühler A., Demongeot J., Mokhtari M., Abdulrazak B., Aloulou H. (eds) Inclusive Smart Cities and e-Health. ICOST 2015. Lecture Notes in Computer Science, vol 9102. Springer, Cham

Abstract

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.

Keywords

Signal fusion method Basic emotions Multimodal detection Physiological signals 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Chaka Koné
    • 1
  • Imen Meftah Tayari
    • 2
  • Nhan Le-Thanh
    • 3
  • Cecile Belleudy
    • 1
  1. 1.University of Nice Sophia Antipolis, LEAT laboratory CNRS UMR 7248Sophia AntipolisFrance
  2. 2.REGIM laboratoryUniversity of SfaxSfaxTunisia
  3. 3.University of Nice Sophia Antipolis, I3S laboratory CNRS UMR 7271Sophia AntipolisFrance

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