DOI QR코드

DOI QR Code

Convergence Study on the Three-dimensional Educational Model of the Functional Anatomy of Facial Muscles Based on Cadaveric Data

카데바 자료를 이용한 얼굴근육의 해부학적 기능 학습을 위한 삼차원 교육 콘텐츠 제작과 관련된 융합 연구

  • Lee, Jae-Gi (Department of Dental Hygiene, Namseoul University)
  • 이재기 (남서울대학교 치위생학과)
  • Received : 2021.07.02
  • Accepted : 2021.09.20
  • Published : 2021.09.28

Abstract

This study dissected and three-dimensionally (3D) scanned the facial muscles of Korean adult cadavers, created a three-dimensional model with realistic facial muscle shapes, and reproduced facial expressions to provide educational materials to allow the 3D observation of the complex movements of cadaver facial muscles. Using the cadavers' anatomical photo data, 3D modeling of facial muscles was performed. We produced models describing four different expressions, namely sad, happy, surprised, and angry. We confirmed the complex action of the 3D cadaver facial muscles when making various facial expressions. Although the results of this study cannot confirm the individual functions of facial muscles quantitatively, we were able to observe the realistic shape of the cadavers' facial muscles, and produce models that would show different expressions depending on the actions performed. The data from this study may be used as educational materials when studying the anatomy of facial muscles.

이 연구는 한국인 성인 시신의 얼굴근육을 해부하고 삼차원 스캔하여, 사실적인 얼굴근육의 형태를 삼차원 오브젝트를 만들고, 이를 통해 표정을 재현하여 카데바 얼굴근육의 복합적인 움직임을 삼차원적으로 관찰 가능한 교육 자료를 제작하는데 목적이 있다. 카데바 해부 사진 자료를 이용하여, 얼굴근육에 대해 삼차원 모델링 하였고, 네 가지 표정(슬픔, 미소, 놀람, 분노)에 따라 얼굴근육이 변화하는 삼차원 영상을 제작하였다. 이를 통해, 삼차원으로 구현한 카데바 얼굴근육의 복합적인 작용과 다양한 표정 변화를 확인할 수 있었다. 이 연구결과는 얼굴근육의 개별적인 기능에 대한 정량적인 자료를 확인할 수는 없지만, 사실적이고 입체적인 카데바의 얼굴근육 형태를 관찰할 수 있고, 복합적인 얼굴근육의 작용으로 인한 표정 변화를 확인할 수 있다. 이러한 자료는 얼굴근육의 해부학적 교육 자료로 활용할 수 있을 것으로 기대한다.

Keywords

Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2020R1I1A3061952).

References

  1. T. Marur, Y. Tuna & S. Demirci. (2014). Facial anatomy. Clinics in dermatology, 32(1), 14-23. DOI : 10.1016/j.clindermatol.2013.05.022
  2. B. Bentsianov & A. Blitzer. (2004). Facial anatomy. Clinics in dermatology, 22(1), 3-13. DOI : 10.1016/j.clindermatol.2003.11.011
  3. M. S. Nestor, D. Fischer & D. Arnold. (2020). "Masking" our emotions: Botulinum toxin, facial expression, and well-being in the age of COVID-19. Journal of cosmetic dermatology, 19(9), 2154-2160. DOI : 10.1111/jocd.13569.
  4. R. L. Drake, A. W. Vogl & A. W. M. Mitchell. (2012). Gray's basic anatomy. Philadlephia : Elsevier
  5. N. V. Alfen, H. J. Gilhuis, J. P. Keijzers, S. Pillen & J. P. Van Dijk. (2013). Quantitative facial muscle ultrasound: Feasibility and reproducibility. Muscle and nerve, 48(3), 375-380. DOI : 10.1002/mus.23769
  6. B. M. Logan, P. A. Reynolds & R. T. Hutchings. (2004). McMinn's color atlas of head and neck anatomy 3rd ed. Philadelphia : Elsevier.
  7. I. H. Chung, C. S. Oh, S. H. Han & H. J. Kim. (2011). Human anatomy 5th ed. Seoul : Hyunmoon.
  8. J. S. Kang et al. (2017). Topographic anatomy 3rd ed. Seoul : Korea medical book publishing company.
  9. S. standring. (2016). Gray's anatomy: The anatomical basis of clinical practice 41st ed. London : Elsveier.
  10. M. Schuenke, E. Schulte & U. Schumacher. (2016). Thieme atlas of anatomy 2nd ed. New York : Thieme
  11. T. Kanade, J. F. Cohn & Y. L. Tian. (2000, March). Comprehensive database for facial expression analysis. Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580) (pp. 46-53). France : Institute of Electrical and Electronics engineers. DOI: 10.1109/AFGR.2000.840611
  12. Y. J. Hong, S. E. Choi, G. P. Nam, H. Choi, J. Cho & I. J. Kim. (2020). Adaptive 3D model-based facial expression synthesis and pose frontalization. Sensors, 20(9), 2578. DOI : 10.3390/s20092578
  13. E. Kuramoto, S. Yoshinaga, H. Nakao, S. Nemoto & Y. Ishida. (2019). Characteristics of facial muscle activity during voluntary facial expressions: Imaging analysis of facial expressions based on myogenic potential data. Neuropsychopharmacology Reports, 39(3), 183-193. DOI: 10.1002/npr2.12059
  14. M. Eskes, M. J. van Alphen, A. J. Balm, L. E. Smeele, D. Brandsma & F. van der Heijden. (2017). Predicting 3D lip shapes using facial surface EMG. Public Library of Science one, 12(4), e0175025. DOI: 10.1371/journal.pone.0175025
  15. M. A. Shiffman. (2012). Muscles used in facial expression. Advanced surgical facial rejuvenation. Berlin : Springer. DOI : 10.1007/978-3-642-17838-2_4
  16. J. G. Lee et al. (2015). Quantitative Anatomical Analysis of Facial Expression Using a 3D Motion Capture System: Application to Cosmetic Surgery and Facial Recognition. Clinical Anatomy, 28(6), 735-744. DOI : 10.1002/ca.22542
  17. H. K. Ahn et al. (2013). Human anatomy 4th ed. Seoul : Komoonsa Publishing.
  18. M. A. Choi et al. (2017). Essentials of anatomy and physiology. Seoul : Elsevier Korea LLC.
  19. Y. B. Huh et al. (2020). Human anaotmy. Seoul : Medical education Publishing.
  20. C. B. Pamela et al. (2005). Oxford Textbook of functional anatomy 2nd ed. New York : Oxford University Press.
  21. M. C. Uchida et al. (2018). Identification of muscle fatigue by tracking facial expressions. Public Library of Science one, 13(12), e0208834. DOI : 10.1371/journal.pone.0208834
  22. H. Bello, B. Zhou & P. Lukowicz. (2020). Facial muscle activity recognition with reconfigurable differential stethoscope-microphones. Sensors, 20(17), 4904. DOI : 10.3390/s20174904
  23. K. L. Schmidt & J. F. Cohn. (2001). Human facial expressions as adaptations: Evolutionary questions in facial expression research. American journal of physical anthropology, 33, 3-24. DOI : 10.1002/ajpa.2001
  24. M. Polo. (2008). Botulinum toxin type A (Botox) for the neuromuscular correction of excessive gingival display on smiling (gummy smile). American journal of orthodontics and dentofacial orthopedic, 133(2), 195-203. DOI : 10.1016/j.ajodo.2007.04.033.
  25. M. de Maio. (2018). Myomodulation with injectable fillers: An innovative approach to addressing facial muscle movement. Aesthetic plastic surgery, 42(3), 798-814. DOI : 10.1007/s00266-018-1116-z
  26. J. Gousheh & E. Arasteh. (2011). Treatment of facia paralysis: dynamic reanimation of spontaneous facial expression-apropos of 655 patients. Plastic and reconstructive surgery, 128(6), 693e-703e. DOI : 10.1097/PRS.0b013e318230c58f
  27. J. Y. Jung, S. J. Seo, Y. J. Han & H. S Jung. (2020). A study on language anxiety and learning achievement through immersive virtual reality english conversation learning program. Journal of the Korea Convergence Society, 11(1), 119-130. DOI : 10.15207/JKCS.2020.11.1.119
  28. J. W. An. (2019). Technology acceptance and influencing factor of anatomy learning using augmented reality: Usability based on the technology acceptance model. Journal of the Korea Convergence Society, 10(12), 487-494. DOI : 10.15207/JKCS.2019.10.12.487
  29. K. S. Lee, W. B. Lim & Y. L. Moon. (2018). AR monitoring technology for medical convergence. Journal of the Korea Convergence Society, 9(2), 119-124. DOI : 10.15207/JKCS.2018.9.2.119