Research on a Calculation Model of Ankle-Joint-Torque-Based sEMG

Area of Science:

• Biomechanics
• Rehabilitation Engineering
• Biomedical Signal Processing

Background:

• Ankle joint movement prediction is crucial for developing advanced rehabilitation equipment.
• Surface electromyography (sEMG) provides valuable data for understanding muscle activity during joint motion.
• The Hill model offers a framework for analyzing muscle-induced joint torques.

Purpose of the Study:

• To establish a prediction model for ankle joint movements.
• To analyze the impact of sEMG on ankle joint torque prediction.
• To provide a theoretical basis for ankle rehabilitation control systems.

Main Methods:

• Utilized surface electromyography (sEMG) data and the Hill model to calculate ankle joint torque.
• Collected and analyzed sEMG, joint angle, and movement signals from nine subjects during various ankle motions.
• Employed a genetic algorithm to optimize 16 physiological parameters within the Hill model.

Main Results:

• The optimized Hill model precisely calculated ankle joint torque with a root mean square error under 1.4 compared to measured torque.
• The model demonstrated accuracy in predicting torque patterns during dorsiflexion/flexion, varus, and internal/external rotation.
• Physiological parameters were identified and optimized using experimental data and a genetic algorithm.

Conclusions:

• The developed ankle movement prediction model offers a solid theoretical foundation for ankle rehabilitation control.
• Accurate torque prediction enhances the efficacy of robotic systems used in ankle rehabilitation.
• The findings support the advancement of intelligent rehabilitation equipment for ankle impairments.