Utilizing an Artificial Neural Network for Musculotendon Actuator Force Estimation

Abstract

Background: Since muscle force depends on various factors, presenting explicit equations for predicting it is difficult. Due to the importance of these equations in different musculoskeletal problems and analysis of motion, proposing solutions for estimating muscle force is indisputable. Although numerous relations have been suggested, the complexity of relations between the caused musculotendon force and the effective factors leads to a lot of problems in suggesting efficient comprehensive computational equations. Thus, the previous proposed relations either do not have universality for estimating muscle force, or are too nonlinear, massive and inefficient. Methods: In this study, after reviewing previous mathematical relations for predicting muscle force, an artificial neural network was implemented to predict force in musculotendon actuators. Findings: The present study provided an appropriate and computationally efficient mathematical model with limited parameters. The model can be used to determine the forces generated by musculotendon actuators in various lengths and contraction velocities. Conclusion: Utilizing a neural network lets us estimate muscle force quicker and more accurately. This could fundamentally increase the computational efficiency in different musculoskeletal problems.

Keywords

• Artificial neural network
• Mathematical modeling
• Musculotendon actuator muscle model