Background: The assessment of limb joint torque is essential for understanding musculoskeletal system dynamics. Yet, the lack of direct muscle strength measurement techniques has prompted previous research to deploy joint torque estimation using machine learning models. These models often suffer from reduced estimation accuracies due to the presence of redundant and irrelevant information within the rapidly expanding complex biomedical datasets as well as suboptimal hyperparameters configurations.
Methods: This study utilized a random forest regression (RFR) model to estimate elbow flexion torque using mechanomyography (MMG) signals recorded during electrical stimulation of the biceps brachii (BB) muscle in 36 right-handed healthy subjects. Given the significance of both feature engineering and hyperparameter tuning in optimizing RFR performance, this study proposes a hybrid method leveraging the General Learning Equilibrium Optimizer (GLEO) to identify most informative MMG features and tune RFR hyperparameters. The performance of the GLEO-coupled with the RFR model was compared with the standard Equilibrium Optimizer (EO) and other state-of-the-art algorithms in physical and physiological function estimation using biological signals.
Results: Experimental results showed that selected features and tuned hyperparameters demonstrated a significant improvement in root mean square error (RMSE), coefficient of determination (R) and slope with values improving from 0.1330 to 0.1174, 0.7228 to 0.7853 and 0.6946 to 0.7414, respectively for the test dataset. Convergence analysis further revealed that the GLEO algorithm exhibited a superior learning capability compared to EO.
Conclusion: This study underscores the potential of the hybrid GLEO approach in selecting highly informative features and optimizing hyperparameters for machine learning models. These advancements are essential for evaluating muscle function and represent a significant advancement in musculoskeletal biomechanics research.
