Advertisement

Analysis of Facial EMG Signal for Emotion Recognition Using Wavelet Packet Transform and SVM

  • Vikram Kehri
  • Rahul Ingle
  • Sangram Patil
  • R. N. Awale
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 748)

Abstract

Emotion recognition has been improved recently and effectively used in medical and diagnostic areas. Automatic recognition of facial expressions is an important application in human–computer interface (HCI). This paper proposed techniques for recognizing three different facial expressions such as happiness, anger, and disgust. Facial signals were recorded using two-channel wireless data acquisition system. Recorded facial EMG signals from zygomatic and corrugator face muscles were set up in four steps: Feature extraction, features selection, classification, and emotion recognition. The features have been extracted using wavelet packet transform method and feed to support vector machine for the classification of three different facial emotions. Finally, the proposed methodology gives classification accuracy 91.66% on 12 subjects.

Keywords

Facial electromyogram (FEMG) Human–computer interface (HCI) Wavelet packet transform (WPT) Support vector machine (SVM) 

Notes

Ethical Approval and Consent to Participate

Ethical Approval is taken from the Ethical committee constituted by this institute. The committee consists of following persons:

Sr. no.

Name of member

Address

Designation in ethical committee

Wheather affiliated to institute

01

Dr. Arya Desh Deepak

Department of Biochemistry, MGM Medical College, Kamothe, New Mumbai

Chairman

No

02

Dr. K. S. Yadav

Associate Professor, Department of Biochemistry, D. Y. Patil Medical College and Hospital, New Mumbai

Member

No

03

Mr. Nitin Yashwante

MSW, Social Worker at D. Y. Patil Medical College and hospital

Medical social worker

No

04

Ms. Pranjal Nayer

Assistant Professor, College of Law, New Mumbai

Legal person

No

05

Dr. Sharukh Tare

Management Teaching Institute

Common man’s representative

No

06

Dr. Neeraj Rawani

Professor, Department of Psychiatry, Terna Medical College, New Mumbai

Member

No

07

Dr. Raval Awale

Professor, Electrical Engineering Department, VJTI Mumbai

Member secretary

Yes

Written content has already taken from all participants. This consent is kept at the R&D Cell of the authors’ institution. This can be made available at any time during the review process.

References

  1. 1.
    Ang, L.B.P., Belen, E.F., Bernardo, R.A., Boongaling, E.R., Briones, G.H., Coronel, J.B.: Facial expression recognition through pattern analysis of facial muscle movements utilizing electromyogram sensors. In: TENCON 2004 IEEE Region 10 Conference, vol. 3, pp. 600–603 (2004)Google Scholar
  2. 2.
    Haag, A., Goronzy, S., Schaich, P., Williams, J.: Emotion recognition using bio-sensors: first steps towards an automatic system. Affect. Dialogue Syst. (2004)Google Scholar
  3. 3.
    Firoozabadi, M.S.P, Oskoei, M.R.A., Hu, H.: A human-computer interface based on forehead multi-channel bio-signals to control a virtual wheelchair, Tarbiat Modares University, Tehran, Iran (2008)Google Scholar
  4. 4.
    Huang, C.N., Chen, and H.Y. Chung: The review of applications and measurements in facial electromyography. J. Med. Biol. Eng. 25, 15–20 (2004)Google Scholar
  5. 5.
    Buenaposada, J., Mu nez, E., Baumela, L.: Recognising facial expressions in video sequences. Pattern Anal. Appl. (2008)Google Scholar
  6. 6.
    Kale, S.N., Dudul, S.V.: Intelligent noise removal from EMG signals using focused time lagged recurrent neural network. Appl. Comput. Intell. Soft Comput. (2009)Google Scholar
  7. 7.
    Hargrove, L., Englehart, K., Hudgins, B.: A comparison of surface and intramuscular myoelectric signal classification. IEEE Trans. Biomed. Eng. 54(5), 847–853 (2007)CrossRefGoogle Scholar
  8. 8.
    Hamedi, M., Salleh, S.H., Sweev, T.T., Kamarulafizam: Surface electromyography-based facial expression recognition in bi-polar configuration. J. Comput. Sci. 7(9), 1407–1415. ISSN 1549-3636 © 2011 Science Publications (2011)Google Scholar
  9. 9.
    Huang, C.-N., Chen, C.-H., Chung, H.-Y.: The review of applications and measurements in facial electromyography. J. Med. Biol. Eng. 25(1), 15–20 (2004)Google Scholar
  10. 10.
    Englehart, K., Hudgin, B., Parker, P.: A wavelet based continuous classification scheme for multi-function myoelectric control. IEEE Trans. Biomed. Eng. 48, 302–311 (2001)CrossRefGoogle Scholar
  11. 11.
    Chu, J.U., Moon, I., Lee, Y.J., Kim, S.K., Mun, M.S.: A supervised feature-projection-based real time EMG pattern recognition for multifunction myoelectric hand control. Trans. Mech. IEEE/ASME 12, 282–290 (2007)CrossRefGoogle Scholar
  12. 12.
    Thulkar, D., Hamde, S.T.: Facial electromyography for characterization of emotions using lab VIEW. In: IEEE International Conference on Industrial Instrumentation and Control (IClC) (2015)Google Scholar
  13. 13.
    Mohammad Rezazadeh, I., Wan, X., Wang, R., Firoozabadi, M.: Toward affective handsfree human machine interface approach in virtual environments-based equipment operation training. In: 9th International Conference on Construction Applications of Virtual Reality, 5–6 Nov 2009Google Scholar
  14. 14.
    Oskoei, M.A., Hu, H.: Application of support vector machines in upper limb motion classification using myoelectric signals. In: Proceedings of the IEEE International Conference on Robotics and Biomimetics. IEEE Xplore Press, Sanya, pp. 388–393, 15–18 Dec 2007Google Scholar
  15. 15.
    Oskoei, M.A., Hu, H.: Support vector machine-based classification scheme for myoelectric control applied to upper limb. IEEE Trans. Biomed. Eng. (2008)Google Scholar
  16. 16.
    Foster, I., Kesselman, C.: The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (1999)Google Scholar
  17. 17.
    Bayram, K.S., Kizrak, M.A., Bolat, B.: Classification of EEG signals by using support vector machines. In: IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA), pp. 1–3. IEEE (2013)Google Scholar
  18. 18.
    Mohammad Rezazadeh, I., Wan, X., Wang, R., Firoozabadi, M.: Toward affective handsfree human machine interface approach in virtual environments-based equipment operation training. In: 9th International Conference on Construction Applications of Virtual Reality, 5–6 Nov 2009Google Scholar
  19. 19.
    Ang, L.B.P., Belen, E.F., Bernardo, R.A., Boongaling, E.R., Briones, G.H., Coronel, J.B.: Facial expression recognition through pattern analysis of facial muscle movements utilizing electromyogram sensors. In: TENCON 2004 IEEE Region 10 Conference, volume C (2004)Google Scholar
  20. 20.
    Hamedi, M., Salleh, S.-H., Swee, T.T., Kamarulafizam.: Surface electromyography-based facial expression recognition in bi-polar configuration. J. Comput. Sci. 7(9), 1407–1415 (2011)Google Scholar
  21. 21.
    Sinha, R., Parsons, O.A.: Multivariate response patterning of fear. Conf. Cognit. Emot. 10(2), 173–198 (1996)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Vikram Kehri
    • 1
  • Rahul Ingle
    • 1
  • Sangram Patil
    • 1
  • R. N. Awale
    • 1
  1. 1.Department of Electrical EngineeringVeermata Jijabai Technological InstituteMumbaiIndia

Personalised recommendations