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Emotion Understanding Using Multimodal Information Based on Autobiographical Memories for Alzheimer’s Patients

  • Juan Manuel Fernandez MontenegroEmail author
  • Athanasios Gkelias
  • Vasileios Argyriou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10116)

Abstract

Alzheimer Disease (AD) early detection is considered of high importance for improving the quality of life of patients and their families. Amongst all the different approaches for AD detection, significant work has been focused on emotion analysis through facial expressions, body language or speech. Many studies also use the electroencephalogram in order to capture emotions that patients cannot physically express. Our work introduces an emotion recognition approach using facial expression and EEG signal analysis. A novel dataset was created specifically to remark the autobiographical memory deficits of AD patients. This work uses novel EEG features based on quaternions, facial landmarks and the combination of them. Their performance was evaluated in a comparative study with a state of the art methods that demonstrates the proposed approach.

Keywords

Support Vector Machine Facial Expression Emotion Recognition Autobiographical Memory Alzheimer Disease Patient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Supplementary material

426013_1_En_17_MOESM1_ESM.avi (12.1 mb)
Supplementary material 1 (avi 12433 KB)

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Juan Manuel Fernandez Montenegro
    • 1
    Email author
  • Athanasios Gkelias
    • 2
  • Vasileios Argyriou
    • 1
  1. 1.Kingston UniversityKingston upon ThamesEngland
  2. 2.Imperial CollegeLondonEngland

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