The influence of modeling hypothesis and experimental methodologies in the accuracy of muscle force estimation using EMG-driven models

Abstract

This paper discusses some methodological questions regarding the application of EMG-driven models to estimate muscle forces, for the triceps surae performing isometric contractions. Ankle torque is estimated from a Hill-type muscle model driven by EMG data, collected from the three components of triceps surae and tibialis anterior. Ankle joint torque is synchronously collected from a dynamometer, which is compared to the sum of each muscle force multiplied by the respective ankle moment arm. A protocol consisting of two steps of low and medium/high loads is used. Raw EMG signal is processed and used as the input signal for the muscle model. The difference between simulated and dynamometer measured torque is calculated as the RMS error between the two curves. A set of nominal muscle model parameters is initially chosen from literature (e.g., OpenSim), which allows observing the characteristics of the error distribution. One possibility to improve model accuracy is using individual muscle parameters. We investigated the effect of applying simple scale factors to the nominal muscle model parameters and using ultrasound for estimating muscle maximum force. Other questions regarding muscle model improvements are also addressed, such as using a nonlinear formulation of activation dynamics and variable pennation angle. Surface EMG signals acquisition and processing can also affect force estimation accuracy. Electrodes positioning can influence signal amplitude, and the one-channel EMG may not represent actual excitation for the whole muscle. We have shown that high density EMG reduces, in some cases, the torque estimation error.