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Evaluation of Classifiers for Emotion Detection While Performing Physical and Visual Tasks: Tower of Hanoi and IAPS

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Intelligent Systems and Applications (IntelliSys 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 868))

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Abstract

With the advancement in robot technology, smart human-robot interaction is of increasing importance for allowing the more excellent use of robots integrated into human environments and activities. If a robot can identify emotions and intentions of a human interacting with it, interactions with humans can potentially become more natural and effective. However, mechanisms of perception and empathy used by humans to achieve this understanding may not be suitable or adequate for use within robots. Electroencephalography (EEG) can be used for recording signals revealing emotions and motivations from a human brain. This study aimed to evaluate different machine learning techniques to classify EEG data associated with specific affective/emotional states. For experimental purposes, we used visual (IAPS) and physical (Tower of Hanoi) tasks to record human emotional states in the form of EEG data. The obtained EEG data processed, formatted and evaluated using various machine learning techniques to find out which method can most accurately classify EEG data according to associated affective/emotional states. The experiment confirms the choice of a method for improving the accuracy of results. According to the results, Support Vector Machine was the first, and Regression Tree was the second best method for classifying EEG data associated with specific affective/emotional states with accuracies up to 70.00% and 60.00%, respectively. In both tasks, SVM was better in performance than RT.

Weka 3: www.cs.waikato.ac.nz/ml/weka/.

The European Clearing House for Open Robotics Development: www.echord.info.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Prince of Songkla University, Hatyai, Thailand

    Shahnawaz Qureshi

  2. Department of Computer Science, Linnaeus University, Växjö, Sweden

    Johan Hagelbäck

  3. BrightWare, Riyadh, Saudi Arabia

    Syed Muhammad Zeeshan Iqbal

  4. Jinnah International Hospital, Abbottabad, Pakistan

    Hamad Javaid

  5. Intelligent Sensing Laboratory, CSIRO, Canberra, Australia

    Craig A. Lindley

Authors
  1. Shahnawaz Qureshi
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  2. Johan Hagelbäck
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  3. Syed Muhammad Zeeshan Iqbal
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  4. Hamad Javaid
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  5. Craig A. Lindley
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Corresponding authors

Correspondence to Shahnawaz Qureshi or Syed Muhammad Zeeshan Iqbal .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, UK

    Rahul Bhatia

Appendix A: Questionnaire

Appendix A: Questionnaire

The questionnaire below was used as Self-Assessment Manikin (SAM) during the study for each subject. Subject Number was an anonymous number.

figure a

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Qureshi, S., Hagelbäck, J., Iqbal, S.M.Z., Javaid, H., Lindley, C.A. (2019). Evaluation of Classifiers for Emotion Detection While Performing Physical and Visual Tasks: Tower of Hanoi and IAPS. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 868. Springer, Cham. https://doi.org/10.1007/978-3-030-01054-6_25

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