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Classification of Physiological Data for Emotion Recognition

  • Philip Gouverneur
  • Joanna Jaworek-KorjakowskaEmail author
  • Lukas Köping
  • Kimiaki Shirahama
  • Pawel Kleczek
  • Marcin Grzegorzek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10245)

Abstract

Emotion recognition is seen to be important not only for computer science or sport activity but also for old and sick people to live independently in their own homes as long as possible. In this paper Empatica E4 wristband is used to collect the date and assess the stress level of the user. We describe an algorithm for the classification of physiological data for emotion recognition. The algorithm has been divided into the following steps: data acquisition, signal preprocessing, feature extraction, and classification. The data acquired during various daily activities consist of more than 3 h of wristband signal. Through various stress tests we achieve a maximum accuracy of 71% for a stressed/relaxed classification. These results lead to the conclusion that Empatica E4 wristband can be used as a device for emotion recognition.

Keywords

Support Vector Machine Random Forest Linear Discriminant Analysis Emotion Recognition Skin Conductance 
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.

Notes

Acknowledgement

Research and development activities leading to this article have been supported by the German Federal Ministry of Education and Research within the project “Cognitive Village: Adaptively Learning Technical Support System for Elderly” (Grant Number: 16SV7223K).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Philip Gouverneur
    • 1
  • Joanna Jaworek-Korjakowska
    • 2
    Email author
  • Lukas Köping
    • 1
  • Kimiaki Shirahama
    • 1
  • Pawel Kleczek
    • 2
  • Marcin Grzegorzek
    • 1
    • 3
  1. 1.Research Group for Pattern RecognitionUniversity of SiegenSiegenGermany
  2. 2.Department of Automatics and Biomedical EngineeringAGH University of Science and TechnologyKrakowPoland
  3. 3.Faculty of Informatics and CommunicationUniversity of Economics in KatowiceKatowicePoland

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