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

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.