College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China.
State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha 410083, China.
Sensors (Basel). 2024 May 2;24(9):2906. doi: 10.3390/s24092906.
The purpose of this article is to establish a prediction model of joint movements and realize the prediction of joint movemenst, and the research results are of reference value for the development of the rehabilitation equipment. This will be carried out by analyzing the impact of surface electromyography (sEMG) on ankle movements and using the Hill model as a framework for calculating ankle joint torque. The table and scheme used in the experiments were based on physiological parameters obtained through the model. Data analysis was performed on ankle joint angle signal, movement signal, and sEMG data from nine subjects during dorsiflexion/flexion, varus, and internal/external rotation. The Hill model was employed to determine 16 physiological parameters which were optimized using a genetic algorithm. Three experiments were carried out to identify the optimal model to calculate torque and root mean square error. The optimized model precisely calculated torque and had a root mean square error of under 1.4 in comparison to the measured torque. Ankle movement models predict torque patterns with accuracy, thereby providing a solid theoretical basis for ankle rehabilitation control. The optimized model provides a theoretical foundation for precise ankle torque forecasts, thereby improving the efficacy of rehabilitation robots for the ankle.
本文旨在建立关节运动的预测模型，实现关节运动的预测，研究成果对康复设备的开发具有参考价值。这将通过分析表面肌电图（sEMG）对踝关节运动的影响，并使用 Hill 模型作为计算踝关节扭矩的框架来实现。实验中使用的表格和方案基于通过模型获得的生理参数。对 9 名受试者在背屈/跖屈、内翻/外翻、内收/外展过程中的踝关节角度信号、运动信号和 sEMG 数据进行了数据分析。使用遗传算法对 Hill 模型确定的 16 个生理参数进行了优化。进行了三项实验以确定最佳模型来计算扭矩和均方根误差。与测量的扭矩相比，优化后的模型精确计算了扭矩，其均方根误差在 1.4 以下。踝关节运动模型可以准确预测扭矩模式，从而为踝关节康复控制提供了坚实的理论基础。优化后的模型为精确的踝关节扭矩预测提供了理论基础，从而提高了康复机器人对踝关节的治疗效果。
