다양한 외부 자극에 따른 생체 정보 변화와 감정 분류 연구 동향

Research trends on Biometric information change and emotion classification in relation to various external stimulus

  • 김기환 (동서대학교 일반대학원 컴퓨터공학과) ;
  • 이훈재 (동서대학교 소프트융합대학 컴퓨터공학부) ;
  • 이영실 (동서대학교 소프트융합대학 컴퓨터공학부) ;
  • 김태용 (동서대학교 소프트융합대학 컴퓨터공학부)
  • Kim, Ki-Hwan (Dept. of Computer Engineering, Dongseo University Graduate School) ;
  • Lee, Hoon-Jae (Div. of Computer Engineering, College of Software Convergence, Dongseo University) ;
  • Lee, Young Sil (Div. of Computer Engineering, College of Software Convergence, Dongseo University) ;
  • Kim, Tae Yong (Div. of Computer Engineering, College of Software Convergence, Dongseo University)
  • 투고 : 2019.01.08
  • 심사 : 2019.03.31
  • 발행 : 2019.03.31

초록

현대인들은 불안정한 소득과 타인과의 갈등 등 다양한 요소로 인하여 정신건강 관리가 필요하다는 주장이 있다. 최근에는 웨어러블 장비에 심전도(Electrocardiogram, ECG)를 측정할 수 있는 장비가 보급되고 있으며, 해외의 경우 의학적 보조수단으로 활용된 사례를 볼 수 있다[14]. 이와 같은 기능을 활용하는 것으로 대표적인 감정(기쁨, 슬픔, 분노 등)을 객관적인 수치로 구별하는 연구들이 진행되고 있다. 그러나 대부분의 연구는 제한적인 환경에서 복합적인 생체 신호를 수집하는 것으로 정확도를 높이고 있다. 따라서 각각의 자극에 대한 생체 정보의 변화와 판별에 가장 많은 영향을 미친 요소를 살펴본다.

Modern people argue that mental health care is necessary because of various factors such as unstable income and conflict with others. Recently, equipments capable of measuring electrocardiogram (ECG) in wearable equipment have been widely used. In the case of overseas, it can be seen as a medical assistant [14]. By using such functions, studies are being conducted to distinguish representative emotions (joy, sadness, anger, etc.) with objective values. However, most studies are increasing accuracy by collecting complex bio-signals in a limited environment. Therefore, we examine the factors that have the greatest influence on the change and discrimination of biometric information on each stimulus.

키워드

참고문헌

  1. 이현민, & 김욱진. (2018). 일인가구의 대인관계와 삶의 만족 및 우울의 구조적 관계. Korean Journal of Social Welfare Studies, 49(3), pp. 147-177. https://doi.org/10.16999/kasws.2018.49.3.147
  2. Min, Kyungsun. (2018, Aug). Leisure and Life Satisfaction among the Work-Life Balance Generation. Journal of the Korean society for Wellness. 13(3), pp. 377-388. https://doi.org/10.21097/ksw.2018.08.13.3.377
  3. Lee, Kyung-Hee. (2012, May). The Problem of Pain and Hunger in Descartes's Moral Theory - From Metaphysics of Mind to Moral Philosophy of Human beings. Philosophical Investigation. 31, pp. 33-54. Available : http://www.dbpia.co.kr/Article/NODE01941264
  4. Alex Rosenberg. (2016, July). Why You Don't Know Your Own Mind. The New Work Times. Available : https://www.nytimes.com/2016/07/18/opinion/why-you-dont-know-your-own-mind.html
  5. Jin-young Choi, Hyung-shin Kim. "Study on Heart Rate Variability and PSD Analysis of PPG Data for Emotion Recognition," Journal of Digital Contents Society, Vol.19, No.1, pp.103-112, Jan, 2018 https://doi.org/10.9728/dcs.2018.19.1.103
  6. Sung Soo Park, Kun Chang Lee. (2018). Analysis of the Relative Importance of HRV Metrics to Predict Emotion by Using Valence-Arousal Driven Neural Network. The Journal of Korean Institute of Information Technology, 16(4), 1-9.
  7. Sangsun Park, Dongnyeok Jeung, Geeyoung Noh, Jundong Cho. (2014). A study on the human emotion infer in meaningful place using HRV signal and text mapping. 한국HCI학회 학술대회, , 253-260.
  8. Min Soo Kim, Jong Soo Kum, Jong Il Park, Dong Gyu Kim, "Research on the Thermal Comfort Heating Mode Considering Psychological and Physiological Response of Automobile Drivers," Korean Journal of Air-Conditioning and Refrigeration Engineering, Vol. 30, No. 3, pp. 149-157, Mar, 2018. https://doi.org/10.6110/KJACR.2018.30.3.149
  9. 김선종. (2017). 국내종합병원의 해외원격의료 활성화 요인에 관한 사례연구 (Doctoral dissertation, 서울대학교 대학원).
  10. Patel, V., Mishra, P., & Patni, J. C. (2018, June). PsyHeal: An Approach to Remote Mental Health Monitoring System. In 2018 International Conference on Advances in Computing and Communication Engineering (ICACCE) (pp. 384-393).
  11. UN Department of Economic and Social Affairs, "World Urbanization Prospects The 2014 Revision", pp. 7, (2014).
  12. Tarvainen, M. P., Niskanen, J., Lipponen, J. A., Rantaaho, P. O., & Karjalainen, P. A. (2014). Kubios HRV - heart rate variability analysis software doi:https://doi.org/10.1016/j.cmpb.2013.07.024
  13. Park, K., & Jeong, H. (2014). Assessing methods of heart rate variability. Korean J Clin Neurophysiol, 16(2), 49-54. https://doi.org/10.14253/kjcn.2014.16.2.49
  14. Malcolm Owen, "ECG feature in Apple Watch is alrea dy saving lives", appleinsider, Dec, 07, 2018. URL : https://appleinsider.com/articles/18/12/07/ecg-feature-in-apple-watch-is-already-saving-lives
  15. Das, Priyanka, Anwesha Khasnobish, and D. N. Tibarewala. "Emotion recognition employing ECG and GSR signals as markers of ANS." 2016 Conference on Advances in Signal Processing (CASP). IEEE, 2016.
  16. Shin, Dongmin, Dongil Shin, and Dongkyoo Shin. "Development of emotion recognition interface using complex EEG/ECG bio-signal for interactive contents." Multimedia Tools and Applications 76.9 (2017): 11449-11470. https://doi.org/10.1007/s11042-016-4203-7
  17. Al-Galal, Sabaa Ahmed Yahya, Imad Fakhri Taha Alshaikhli, and Abdul Wahab Bin Abdul Rahman. "Automatic emotion recognition based on EEG and ECG signals while listening to quranic recitation compared with listening to music." 2016 6th International Conference on Information and Communication Technology for The Muslim World (ICT4M). IEEE, 2016.
  18. Feng, Huanghao, Hosein M. Golshan, and Mohammad H. Mahoor. "A wavelet-based approach to emotion classification using EDA signals." Expert Systems with Applications 112 (2018): 77-86. https://doi.org/10.1016/j.eswa.2018.06.014