功能性电刺激下肌肉疲劳自适应的关节运动控制

Functional Electrical Stimulation (FES) is one of the solutions to improve lost motor functions in persons with Spinal Cord Injury (SCI) or cerebral injury.It is a promising technique to provide active improvement to such patients in terms of mobility, stability and side-effect prevention. FES-elicited muscle force is required to be appropriate and persistent to perform intended movement or maintain a posture balance. However, muscle state changes such as muscle fatigue degrade the performance of FES. In addition, most of complete SCI patients don't have sensory feedback to detect the fatigue. Conventional FES control systems are either in open-loop or not robust to muscle state changes. Therefore, this project aims at a development of joint motion prediction and feedback control method in order to enhance the joint motion control of FES in terms of accuracy, robustness, and safety to the patients. In order to predict FES-induced joint motion, evoked-Electromyography (eEMG) will be applied to correlate the muscle electrical activity and mechanical activity. Characteristic eEMG parameters will be extracted using principal component analysis according to muscle synergy therory. And then an EMG-based joint motion predictive model will be developed. As muscle fatigue usually represents complex, time-variant, subject-speci?c and protocol-speci?c characteristics, a fatigability estimation method will be built based on the dynamic variations of eEMG characteristics. The model paramerters will be identified by Kalman filter to catch the time-variant dynamic properties. A forgeting factor is adopted in the Kalman filter to catch the time-variant dynamic properties of muscle fatigue, which can be determined and adjusted according to the fatigability of muscle. Subsequently, a predictive control strategy will be applied to obtain muscle fatigue-adaptive joint motion. The control performance will be assessed in a constant joint angle maintaing task, where the muscle group is under isometric contraction condition. Finally, both the tracking performance and the robustness to joint angle measurement failure will be inverstigated in different subjects.

恢复和代偿因神经系统损伤而导致的运动功能缺失是康复医疗工程的重要目标。功能性电刺激技术不仅可提高患者的运动功能，还在预防肌肉萎缩等方面具有重要的临床意义。然而，电刺激下肌肉快速疲劳会导致肌力下降，且疲劳本身具有复杂、时变的动态特性，增加了准确控制关节运动的难度，限制了其应用范围。针对目前尚待解决的肌肉疲劳补偿问题，本项目拟基于肌电信号开展肌肉疲劳自适应的关节运动控制研究。首先，基于肌肉协同机制，对协同肌群多通道非平稳的表面肌电信号进行分析处理，利用主元分析的方法提取疲劳肌电特征量，并建立基于肌电特征量的关节运动预测模型；然后，根据肌电特征的动态特性，监测及评估肌肉疲劳的动态特性，利用卡尔曼滤波进行关节运动预测模型的参数辨识；受中枢神经控制系统启发，采用具有前馈和反馈环节的预测控制方法，实现基于肌电信号的疲劳自适应关节运动控制。最后，以维持膝关节的姿态为例对所提出的研究方法进行实验验证。

恢复和代偿因神经系统损伤而导致的运动功能缺失是康复医疗工程的重要目标。功能性电刺激技术不仅可提高患者的运动功能，还在预防肌肉萎缩等方面具有重要的临床意义。然而，电刺激下肌肉快速疲劳会导致肌力下降，且疲劳本身具有复杂、时变的动态特性，增加了准确控制关节运动的难度，限制了其应用范围。针对目前尚待解决的肌肉疲劳补偿问题，本项目开展电刺激下肌肉疲劳及外部干扰自适应的关节运动控制研究。首先，基于肌肉活动和肌肉协同机制，对协同肌群多通道非平稳的表面肌电信号进行分析处理，利用主元分析、独立成分分析等方法对肌电信号进行预处理，提取肌电特征量，基于模式识别与机器学习技术实现了关节运动模式的识别与连续运动的估计；其次，根据基于生理学研究成果建立了肌骨骼系统模型，利用最小二乘等方法对模型参数进行了辨识，并通过了实验验证；最后，提出了基于神经网络滑模控制算法的电刺激控制方法，并以电刺激诱发膝关节运动为例，并对控制方法在肌肉疲劳和外部干扰自适应方面的性能进行了仿真和实验验证。