Dynamic Modeling of the Electromyographic and Masticatory Force Relation Through Adaptive Neuro-Fuzzy Inference System Principal Dynamic Mode Analysis

Document Type: Original Paper

Authors

1 Sadjad University of Mashhad, Mashhad, Iran

2 Rayan Center for Neuroscience and Behavior, Ferdowsi University of Mashhad, Mashhad, Iran

3 Ferdowsi University of Mashhad

4 Electrical Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran

Abstract

Introduction: Researchers have employed surface electromyography (EMG) to study the human masticatory system and the relationship between the activity of masticatory muscles and the mechanical features of mastication. This relationship has several applications in food texture analysis, control of prosthetic limbs, rehabilitation, and teleoperated robots.
Materials and Methods: In this paper, we proposed a model by combining the concept of fuzzy interface systems and principal dynamic mode analysis (PDM). We hypothesized that the proposed approach would provide nonlinear and dynamic characteristics improving the estimation results compared to those obtained by the classical PDM analysis and still having the benefits of a PDM model including the sparse presentation of the system dynamics. After developing PDM, the nonlinear polynomial function of the PDM model was replaced with adaptive neuro-fuzzy inference system (ANFIS) network architecture. After training, the relevant fuzzy rules were extracted and used for creating the fuzzy block (as the nonlinear function block) and predicting the output signal. The proposed approach was later employed to predict bite force using EMG of the temporalis and masseter muscles.
Results: Our proposed method outperformed the classical PDM analysis (in terms of our evaluation criteria) in predicting masticatory force . The inter-subject evaluation of the model performance proved that the model created using the data of one subject could be used for predicting masticatory force in other subjects.
Conclusion: The proposed model can be helpful in food analysis to predict masticatory force based on the electrical activity of the masseter and temporalis muscles.

Keywords

Main Subjects


References

 

  1. Xu W, Borland  JE. Mastication robots: biological inspiration to implementation. Springer-Verlag Berlin Heidelberg Press. 2010.
  2. Smit HJ, Kemsley EK, Tapp HS, Henry CJ. Does prolonged chewing reduce food intake? Fletcherism revisited. Appetite. 2011 Aug 1;57(1):295-8.
  3. Brown WE, Langley KR, Mioche L, Marie S, Gérault S, Braxton D. Individuality of understanding and assessment of sensory attributes of foods, in particular, tenderness of meat. Food Quality and Preference. 1996 Jul 1;7(3-4):205-16.
  4. Plesh O, Bishop B. Effect of gum hardness on chewing pattern. Exp Neurol. 1986; 92: 502-12. 
  5. Helkimo E, Ingervall B. Bite force and functional state of the masticatory system in young men. Swedish Dental journal. 1978; 2: 167–75.
  6. Kalani H, Moghimi S, Akbarzadeh A. Towards an SEMG-based tele-operated robot for masticatory rehabilitation. Comput Biol Med. 2016; 75: 243–56.
  7. Kalani H, Moghimi S, Akbarzadeh A. SEMG-based prediction of masticatory kinematics in rhythmic clenching movements. Biomed Signal Process Control. 2015; 20: 24–34.
  8. Savelberg HCM , Herzog W. Prediction of dynamic tendon forces from electromyographic signals: An artificial neural network approach. J Neurosci Methods. 1997; 78: 65-74.
  9. Luh JJ, Chang GC, Cheng CK, Lai JS, Kuo TS. Isokinetic elbow joint torques estimation from surface EMG and joint kinematic data: using an artificial neural network model. Journal of Electromyography and Kinesiology. 1999 Apr 1;9(3):173-83.
  10. Mobasser F, Hashtrudi-Zaad K. A comparative approach to hand force estimation using artificial neural networks. Biomedical engineering and computational biology. 2012 Jan; 4.
  11. Hashemi J, Morin E, Mousavi P, Mountjoy K,  Hashtrudi-Zaad K. EMG–force modeling using parallel cascade identification. J Electromyography Kinesiol. 2012; 22: 469–77.
  12. Goharian N, Kalani H, Moghimi S. A time-delay parallel cascade identification system for predicting jaw movements.  Biomedical Engineering (ICBME). 2014;  281 –6. DOI: 10.1109/ICBME.2014.7043936
  13. Kalani H, Akbarzadeh A, Moghimi S. Prediction of Clenching jaw Movements Based on EMG Signals Using Fast Orthogonal Search. ICEE 2015.  DOI: 10.1109/IranianCEE.2015.7146175.
  14. Marmarelis VZ. Nonlinear dynamic modeling of physiological systems. Wiley-Interscience. 2004.
  15. Assefi M, Moghimi S, Kalani H, Moghimi A. Dynamic Modeling of SEMG-Force Relation in the Presence of Muscle Fatigue during Isometric Contractions. Biomed Signal Process Control. 2016, 28: 41-9.
  16. Wang L, Buchanan S. Prediction of Joint Moments Using a Neural Network Model of Muscle Activations From EMG Signals. IEEE Trans Neural Syst Rehabil Eng. 2002; 10: 30-7.
  17. Liu H.J,  Young K.Y. Upper-Limb EMG-Based Robot Motion Governing Using Empirical Mode Decomposition and Adaptive Neural Fuzzy Inference System.  J Intell Robot Syst. 2012; 68: 275–91.
  18. Koçer S. Classification of Emg Signals Using Neuro-Fuzzy System and Diagnosis of Neuromuscular Diseases.  J Med Syst. 2010; 34: 321–9.
  19. Subasi A. Classification of EMG signals using combined features and soft computing techniques. Appl Soft Comput. 2012; 12: 2188–98.
  20. Boyacioglua MA, Avcib D. An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange. Expert Syst Appl. 2010; 37: 7908–12.
  21. Güler I, Übeyli E. D. Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. J Neurosci Meth. 2005; 148: 113-21.
  22. Marmarelis VZ, Chon KH, Holstein-Rathlou NH, Marsh DJ. Nonlinear Analysis of Renal Autoregulation in Rats Using Principal Dynamic Modes. Ann Biomed Eng. 1999; 27: 23–31.
  23. Marmarelis VZ, Shin DC, Song D, Hampson RE, Deadwyler SA, Berger TW. On parsing the neural code in the prefrontal cortex of primates using principal dynamic modes. Journal of computational neuroscience. 2014 Jun 1;36(3):321-37.
  24. Korenberg MJ. Parallel cascade identification and kernel estimation for nonlinear systems. Annals of biomedical engineering. 1991 Jul 1;19(4):429-55.
  25. Hashemi J, Morin E, Mousavi P, Hashtrudi-zaad K. Enhanced Dynamic EMG-Force Estimation Through Calibration and PCI Modeling. IEEE Trans Neural Syst Rehabil Eng.  23 (2014) 41-50.