THREE DIFFERENTIAL EMOTION CLASSIFICATION BY MACHINE LEARNING ALGORITHMS USING PHYSIOLOGICAL SIGNALS - Discriminantion of Emotions by Machine Learning Algorithms

Eun-Hye Jang, Byoung-Jun Park, Sang-Hyeob Kim, Jin-Hun Sohn

Abstract

In HCI researches, human emotion classification has done by machine learning algorithms based on physiological signals. The aim of this study is to classify three different emotional states (boredom, pain, and surprise) by 5 machine learning algorithms using features extracted from physiological signals. 200 college students participated in this experiment. The audio-visual film clips were used to provoke emotions and were tested their appropriateness and effectiveness. EDA, ECG, PPG, and SKT as physiological signals were acquired for 1 minute before each emotional state as baseline and for 1-1.5 minutes during emotional state and were analyzed for 30 seconds from the baseline and the emotional state. 23 parameters were extracted from these signals: SCL, NSCR, mean SCR, mean SKT, maximum SKT, sum of negative SKT, and sum of positive SKT, mean PPG, mean RR interval, standard deviation RR interval, mean BPM, RMSSD, NN50, percenet of NN50, SD1, SD2, CSI, CVI, LF, HF, nLF, nHF, and LF/HF ratio. For emotion classification, the difference values of each feature subtracting baseline from the emotional state were used for analysis using 5 machine learning algorithms. The result showed that an accuracy of emotion classification by SOM was lowest and SVM was highest. This could help emotion recognition studies lead to better chance to recognize various human emotions by using physiological signals. Also, it is able to be applied on human-computer interaction system for emotion detection.

References

  1. Wagner, J., Kim, J., Andre, E., 2005. From physiological signals to emotions: Implementing and comparing selected methods for feature extraction and classification, IEEE International Conference on Multimedia and Expo, Amsterdam, pp. 940-943.
  2. Drummond, P. D., Quah, S. H., 2001. The effect of expressing anger on cardiovascular reactivity and facial blood flow in Chinese and Caucasians, Psychophysiology, vol. 38, pp. 190-196.
  3. Ax, A. R., 1953. The physiological differentiation between fear and anger in humans, Psychosomatic Medicine, vol. 15, pp. 147-150.
  4. Boiten, F. A., 1996. Autonomic response patterns during voluntary facial action, Psychophysiology, vol. 33, pp. 123-131.
  5. Kanade, T. C., Tian, Y., 2000. Comprehensive database for facial expression analysis, Proceeding of the 4th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 46-53.
  6. Palomba, D., Sarlo, M., Angrilli, A., Mini, A., 2000. Cardiac responses associated with affective processing of unpleasant film stimulus, International Journal of Psychophysiology, vol. 36, pp. 45-57.
  7. de Melo, C. M., Kenny, P. G., Gratch, J., 2010. Influence of autonomic signals on perception of emotions in embodied agents, Applied Artificial Intelligence, vol. 6, pp. 494-509.
  8. Flor, H., Knost, B., Birbaumer, N., 2002. The role of operant conditioning in chronic pain: an experimental investigation pain, Pain, vol. 95(1-2), pp. 111-118.
  9. Jolliffe, C. D., Nicholas, M. K., 2004. Verbally reinforcing pain reports: an experimental test of the operant model of chronic pain, Pain, vol. 107(1-2), pp. 167-175.
  10. Collet, C., Vernet-Maury, E., Delhomme, G., Dittmar, A., 1997. Autonomic nervous system response patterns specificity to basic emotions, Journal of the Autonomic Nervous System, vol. 62, pp. 45-57.
  11. Nasoz, F., Alvarez, K., Lisetti, C. L., Finkelstein, N., 2004. Emotion recognition from physiological signals using wireless sensors for presence technologies, Cognition, Technology and Work, vol. 6, pp. 4-14.
  12. Verhoef, T., Lisetti, C., Barreto, A., Ortega, F., Zant, T., Cnossen, F., 2009. Bio-sensing for emotional characterization without word labels, HumanComputer Interaction: Ambient, ubiquitous and intelligent interaction, 13th International Conference, San Diego, CA, USA, pp. 693-702.
  13. Arroyo-Palacios, J., Romano, D. M., 2008. Towards a standardization in the use of physiological signals for affective recognition systems, Proceedings of Measuring Behavior 2008.
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Paper Citation


in Harvard Style

Jang E., Park B., Kim S. and Sohn J. (2012). THREE DIFFERENTIAL EMOTION CLASSIFICATION BY MACHINE LEARNING ALGORITHMS USING PHYSIOLOGICAL SIGNALS - Discriminantion of Emotions by Machine Learning Algorithms . In Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8425-95-9, pages 528-531. DOI: 10.5220/0003880605280531


in Bibtex Style

@conference{icaart12,
author={Eun-Hye Jang and Byoung-Jun Park and Sang-Hyeob Kim and Jin-Hun Sohn},
title={THREE DIFFERENTIAL EMOTION CLASSIFICATION BY MACHINE LEARNING ALGORITHMS USING PHYSIOLOGICAL SIGNALS - Discriminantion of Emotions by Machine Learning Algorithms},
booktitle={Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2012},
pages={528-531},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003880605280531},
isbn={978-989-8425-95-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - THREE DIFFERENTIAL EMOTION CLASSIFICATION BY MACHINE LEARNING ALGORITHMS USING PHYSIOLOGICAL SIGNALS - Discriminantion of Emotions by Machine Learning Algorithms
SN - 978-989-8425-95-9
AU - Jang E.
AU - Park B.
AU - Kim S.
AU - Sohn J.
PY - 2012
SP - 528
EP - 531
DO - 10.5220/0003880605280531