감성과학 (Science of Emotion and Sensibility)

  • 제20권3호
  • /
  • Pages.13-22
  • /
  • 2017
  • /
  • 1226-8593(pISSN)
  • /
  • 2383-613X(eISSN)
한국감성과학회 (Korean Society for Emotion and Sensibility)
권호 표지
DOI QR코드

DOI QR Code

이산 웨이브렛 변환을 이용한 고각성 부정 감성의 GSR 신호 분석

Analysis of Galvanic Skin Response Signal for High-Arousal Negative Emotion Using Discrete Wavelet Transform

  • 임현준 (연세대학교 의과대학 의학공학교실) ;
  • 유선국 (연세대학교 의과대학 의학공학교실) ;
  • 장원석 (연세대학교 의과대학 의학공학교실)
  • Lim, Hyun-Jun (Department of Medical Engineering, Yonsei University College of Medicine) ;
  • Yoo, Sun-Kook (Department of Medical Engineering, Yonsei University College of Medicine) ;
  • Jang, Won Seuk (Department of Medical Engineering, Yonsei University College of Medicine)
  • 투고 : 2016.07.28
  • 심사 : 2017.04.19
  • 발행 : 2017.09.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

감성은 의사 결정, 지각 등에 직접적으로 영향을 미치며 인간의 삶에서 중요한 역할을 한다. 본 논문에서는 고각성 부정 감성의 편리하고 정확한 인식에 있어서, 생체신호를 이용한 분석 알고리즘을 설계하고자 한다. 이를 위해 본 연구에서는 보통 / 공포 감성 유발 영상을 이용하여 두 감성을 유도한 후, 생체신호 중 간단한 피부전도도 신호를 측정하였다. 측정된 피부전도도에 대해 Tonic 성분과 Phasic 성분으로 분해하고 감성 자극과 관련된 Phasic 성분을 더 상세하게 SCVSR, SCSR로 분해하여 각 성분의 주요한 특징들을 추출함으로써, 정확한 분석을 하기 위해 기존의 사용된 방법이 아닌 우수한 시간-주파수 지역화 특성을 가진 이산 웨이브렛 변환을 사용하였다. 추출된 특징들은 Phasic 성분의 최댓값, Phasic 성분의 진폭, SCVSR의 영교차율, SCSR의 영교차율이다. 분석 결과, 4가지 특징들 모두 고각성 부정 감성의 경우가 저각성 보통 감성의 경우보다 더 높은 값을 나타내고, 기존의 분석 방법보다 통계적으로 두 감정 사이의 더 유의미한 차이를 확인할 수 있었다. 이에 따라 본 연구의 결과는 피부전도도가 고각성 부정 감성 측정에 대해 유용한 지표라는 것을 확인하였으며, 향후 피부전도도를 이용한 실시간 부정 감성 평가 시스템 개발에 기여할 수 있을 것을 나타낸다.

Emotion has a direct influence such as decision-making, perception, etc. and plays an important role in human life. For the convenient and accurate recognition of high-arousal negative emotion, the purpose of this paper is to design an algorithm for analysis using the bio-signal. In this study, after two emotional induction using the 'normal' / 'fear' emotion types of videos, we measured the Galvanic Skin Response (GSR) signal which is the simple of bio-signals. Then, by decomposing Tonic component and Phasic component in the measured GSR and decomposing Skin Conductance Very Slow Response (SCVSR) and Skin Conductance Slow Response (SCSR) in the Phasic component associated with emotional stimulation, extracting the major features of the components for an accurate analysis, we used a discrete wavelet transform with excellent time-frequency localization characteristics, not the method used previously. The extracted features are maximum value of Phasic component, amplitude of Phasic component, zero crossing rate of SCVSR and zero crossing rate of SCSR for distinguishing high-arousal negative emotion. As results, the case of high-arousal negative emotion exhibited higher value than the case of low-arousal normal emotion in all 4 of the features, and the more significant difference between the two emotion was found statistically than the previous analysis method. Accordingly, the results of this study indicate that the GSR may be a useful indicator for a high-arousal negative emotion measurement and contribute to the development of the emotional real-time rating system using the GSR.

키워드

참고문헌

  1. Aldemir, R., & Tokmakci, M. (2015). Investigation of respiratory and heart rate variability in hypertensive patients. Turkish Journal of Electrical Engineering & Computer Sciences, 23(1), 67-79. DOI: 10.3906/elk-1211-110
  2. Bänziger, T., Grandjean, D., & Scherer, K. R. (2009). Emotion recognition from expressions in face, voice, and body: the Multimodal Emotion Recognition Test (MERT). Emotion, 9(5), 691-704. DOI: 10.1037/a0017088
  3. Bettadapura, V. (2012). Face expression recognition and analysis: the state of the art. arXiv preprint arXiv:1203.6722.
  4. Bradley, M. M., & Lang, P. J. (1994). Measuring emotion: the self-assessment manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry, 25(1), 49-59. DOI:10.1016/0005-7916(94)90063-9
  5. Brosch, T., Scherer, K. R., Grandjean, D. M., & Sander, D. (2013). The impact of emotion on perception, attention, memory, and decision-making. Swiss Medical Weekly, 143, w13786. DOI: 10.4414/smw.2013.13786
  6. Christie, I. C. (2002). Multivariate discrimination of emotion-specific autonomic nervous system activity. Unpublished master's thesis, Virginia Polytechnic Institute and State University, Blacksburg, VA.
  7. Daubechies, I. (1990). The wavelet transform, timefrequency localization and signal analysis. IEEE Transactions on Information Theory, 36(5), 961-1005. DOI: 10.1109/18.57199
  8. Ekman, P. (1992). An argument for basic emotions. Cognition & Emotion, 6(3-4), 169-200. DOI:10.1080/02699939208411068
  9. Ford, J. H., Addis, D. R., & Giovanello, K. S. (2012). Differential effects of arousal in positive and negative autobiographical memories. Memory, 20(7), 771-778. DOI: 10.1080/09658211.2012.704049
  10. Geer, J. H. (1966). Fear and autonomic arousal. Journal of Abnormal Psychology, 71(4), 253-255. DOI:10.1037/h0023544
  11. Goshvarpour, A., Abbasi, A., & Goshvarpour, A. (2014). Impact of Music on College Students: Analysis of Galvanic Skin Responses. Applied Medical Informatics, 35(4), 11-20.
  12. Heino, A., Van der Molen, H.H., & Wilde, G.J.S. (1990). Risk-homeostatic processes in car following behaviour: electrodermal responses and verbal risk estimates as indicators of the perceived level of risk during a car-driving task. Groningen, NL: Traffic Research Center.
  13. Lang, P. J. (1980). Behavioral treatment and biobehavioral assessment: Computer applications.
  14. Lewis, M. (1995). Self-conscious emotions. American scientist, 83(1), 68-78.
  15. Najarian, K., & Splinter, R. (2005). Biomedical signal and image processing. CRC press.
  16. Norton, J. J., Lee, D. S., Lee, J. W., Lee, W., Kwon, O., Won, P., ... & Umunna, S. (2015). Soft, curved electrode systems capable of integration on the auricle as a persistent brain-computer interface. Proceedings of the National Academy of Sciences, 112(13), 3920-3925. DOI: 10.1073/pnas.1424875112
  17. Misiti, M., Misiti, Y., Oppenheim, G., & Poggi, J. M. (1996). Wavelet toolbox. The MathWorks Inc., Natick, MA, 15, 21.
  18. Murry, J. P., & Dacin, P. A. (1996). Cognitive moderators of negative-emotion effects: Implications for understanding media context. Journal of Consumer Research, 22(4), 439-447. DOI: 10.1086/209460
  19. Picard, R. W., Vyzas, E., & Healey, J. (2001). Toward machine emotional intelligence: Analysis of affective physiological state. IEEE transactions on pattern analysis and machine intelligence, 23(10), 1175-1191. DOI: 10.1109/34.954607
  20. Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39, 1161-1178. DOI: 10.1037/h0077714
  21. Singh, N. (2013). Arousal Level Determination in Video Game Playing Using Galvanic Skin Response (Doctoral dissertation, Thapar University, Patiala).
  22. Soleymani, M., Pantic, M., & Pun, T. (2012). Multimodal emotion recognition in response to videos. IEEE transactions on affective computing, 3(2), 211-223. DOI: 10.1109/t-affc.2011.37
  23. Swangnetr, M., & Kaber, D. B. (2013). Emotional state classification in patient–robot interaction using wavelet analysis and statistics-based feature selection. IEEE Transactions on Human-Machine Systems, 43(1), 63-75. DOI: 10.1109/tsmca.2012.2210408
  24. Tarvainen, M. P., Karjalainen, P. A., Koistinen, A. S., & Valkonen-Korhonen, M. V. (2000). Principal component analysis of galvanic skin responses. In Engineering in Medicine and Biology Society, 2000. Proceedings of the 22nd Annual International Conference of the IEEE (Vol. 4, pp. 3011-3014). IEEE. DOI: 10.1109/iembs.2000.901513
  25. Wu, G., Liu, G., & Hao, M. (2010). The analysis of emotion recognition from GSR based on PSO. In Intelligence Information Processing and Trusted Computing (IPTC), 2010 International Symposium on (pp. 360-363). IEEE. DOI: 10.1109/iptc.2010.60
  26. Zhang, W., Meng, X., Li, Z., Lu, Q., & Tan, S. (2015). Emotion Recognition in Speech Using Multiclassification SVM. In Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UICATC- ScalCom), 2015 IEEE 12th Intl Conf on (pp. 1181-1186). IEEE. DOI: 10.1109/uic-atc-scalcomcbdcom- iop.2015.215