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Emotion Analysis Using Heart Rate Data

  • Luis Alberto Barradas Chacon
  • Artem FedoskinEmail author
  • Ekaterina Shcheglakova
  • Sutthida Neamsup
  • Ahmed Rashed
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1062)

Abstract

This paper describes the attempt to classify human emotions without including Electroencephalography (EEG) signal, using the DEAP dataset (A Database for Emotion Analysis using Physiological Signals). Basing our research on the original paper (Koelstra et al. 2012), we claim that emotions (Valence and Arousal scores) can be classified with only one pulse detecting sensor with a comparable result to the classification based on the EEG signal. Moreover, we propose the method to classify emotions avoiding any sensors by extracting the pulse of the person from the video based on the head movements. Using Lucas-Kanade algorithm for optical flow (Balakrishnan et al. 2013), we extract movement signals, filter them, extract principal components, recreate heart rate (HR) signal and then use in the emotion classification. The first part of the project was conducted on the dataset containing 32 participants’ heart rate values (1280 activation cases), while the second part was based on the frontal videos of the participants (874 videos). Results support the idea of one-sensor emotion classification and deny the possibility of zero-sensor classification with the proposed method.

Keywords

Emotion classification DEAP dataset Heart rate estimation Biosignals 

References

  1. Balakrishnan, G., Durand, F., Guttag, J.: Detecting pulse from head motions in video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3430–3437 (2013)Google Scholar
  2. Candra, H., et al.: Investigation of window size in classification of EEG-emotion signal with wavelet entropy and support vector machine. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 7250–7253. IEEE (2015)Google Scholar
  3. Choi, J., Gutierrez-Osuna, R.: Estimating mental stress using a wearable cardio-respiratory sensor. In: Sensors 2010, IEEE, pp. 150–154. IEEE (2010)Google Scholar
  4. Chung, S.Y., Yoon, H.J.: Affective classification using Bayesian classifier and supervised learning. In: 2012 12th International Conference on Control, Automation and Systems, pp. 1768–1771. IEEE (2012)Google Scholar
  5. Ekman, P.: Basic emotions. In: Handbook of Cognition and Emotion, pp. 45–60 (1999)CrossRefGoogle Scholar
  6. Hosseini, S.A., Khalilzadeh, M.A., Changiz, S.: Emotional stress recognition system for affective computing based on bio-signals. J. Biol. Syst. 18(spec01), 101–114 (2010)CrossRefGoogle Scholar
  7. Koelstra, S., et al.: DEAP: a database for emotion analysis; using physiological signals. IEEE Trans. Affect. Comput. 3(1), 18–31 (2012)CrossRefGoogle Scholar
  8. Lazarus, R.S., Speisman, J.C., Mordkoff, A.M.: The relationship between autonomic indicators of psychological stress: heart rate and skin conductance. Psychosom. Med. 25(1), 19–30 (1963)CrossRefGoogle Scholar
  9. Liu, Y., Sourina, O.: EEG-based subject-dependent emotion recognition algorithm using fractal dimension. In: 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3166–3171. IEEE (2014)Google Scholar
  10. Mohan, P.M., Nagarajan, V., Das, S.R.: Stress measurement from wearable photoplethysmographic sensor using heart rate variability data. In: 2016 International Conference on Communication and Signal Processing (ICCSP), pp. 1141–1144. IEEE (2016)Google Scholar
  11. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12(Oct), 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  12. Tripathi, S., Acharya, S., Sharma, R.D., Mittal, S., Bhattacharya, S.: Using deep and convolutional neural networks for accurate emotion classification on DEAP dataset. In: Twenty-Ninth IAAI Conference (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Luis Alberto Barradas Chacon
    • 1
  • Artem Fedoskin
    • 1
    Email author
  • Ekaterina Shcheglakova
    • 1
  • Sutthida Neamsup
    • 1
  • Ahmed Rashed
    • 1
  1. 1.Information Systems and Machine Learning LabUniversity of HildesheimHildesheimGermany

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