【作者】 辜承慰；

【导师】 明东；

【作者基本信息】 天津大学 ， 生物医学工程， 2009， 硕士

【摘要】 近年来,脊髓损伤的发病率呈显著上升的趋势,运用功能性电刺激(FES)技术进行脊髓损伤治疗和功能重建研究正成为生物信息学、康复医学及神经电生理等领域的前沿热点课题,其关键技术之一是FES控制信号的选择。现有的FES控制信号只能被动地由外界给出,其康复模式需要长期训练,无法根据动作状态实时加以反馈调节,因而具有自适应性差、易受干扰、难以学习掌握等致命弱点,制约了FES技术的功能重建效果,成为其在康复临床推广与应用中亟待解决的瓶颈问题。本文从人体动力学和运动学信息出发,提出了一种全新的动力学与运动学双模态FES自动控制方案,并通过对两类控制信号的识别分析验证了该控制方案的可行性。研究中首先设计了一套以单片机微处理器为核心的下肢助行FES系统,包括控制电路、刺激电路和人机界面,通过相关临床实验案例初步验证了该系统对于脊髓损伤的康复治疗和功能重建具有良好效果。用于动力学模态的系统控制信号是来自FES助行过程中步行器测力系统监测到的柄反作用矢量(HRV)信息,文中利用模糊C均值和K均值聚类方法从HRV中提取下肢动作侧向性特征,并对这些特征进行基于交叉验证与支持向量机的分类识别,结果表明在区分左右下肢侧向性信息方面,单个个体的分类正确率最高可达96%,10例实验对象混合分类正确率可达86%;用于运动学模态的系统控制信号是来自FES刺激过程中的膝关节角度信息,文中通过构造适用于下肢FES动作的非线性自回归肌肉模型,设计相关刺激动作实验,建立起了膝关节角度与刺激强度之间的关系,得到该肌肉数学模型的结构与参数,并重点研究了人体运动学信息作为反馈控制信号的神经网络整定PID系统的控制结构和算法,相关实验表明反馈信号与预设角度值之间的误差可控制在5%以内。本文研究结果表明,基于柄反作用矢量与膝关节角度的动力学与运动学双模态信息有望作为肢体刺激侧向性触发与反馈调整的有效信号,用于助行FES系统的自动控制,从而为更先进的下肢人工运动神经假体系统设计提供帮助。更多还原

【Abstract】 In recent years, the incidence of spinal cord injury (SCI) disease reveals a general trend of fast increase. It has been a hot topic in bioinformatics, rehabilitation medicine and electro-neurophysiology using functional electrical stimulation (FES) for SCI therapy and function restoration. One of FES key techniques is its control signals choosing. Existing FES control signals are usually given by outside environment passively. The therapy modes need long term training and cannot present feedback to modify the stimulation level according to movement conditions, which leads to many disadvantages, such as poor self-adaptability, great disturbance and hard exercitation, and constrains the function restoration effect of FES. This problem has been the bottleneck to the popularization and application in clinics and should be solved urgently.From the viewpoint of human kinetics and kinematics information, the thesis presented a new automatic FES control method based on dual-modal of kinetics and kinematics and tested its feasibility through the recognition and analysis for both control signals. A FES system of lower limbs for assisted walking was designed based on chip microprocessor, which included control circuit, stimulation circuit and human machine interface. The effects of this system on SCI rehabilitation therapy and function restoration were preliminarily tested through clinic trails. The system control signal for kinetics mode was the handle reaction vector (HRV) collected from a walker dynamometer system during FES-assisted walking. The lateral features of lower limbs movement were extracted from HRV through fuzzy C-means and K-means clustering analytical method and recognized by K-fold cross-validation and support vector machine. The results showed that the highest recognition rate reached 96% for individual and 86% for group of 10 subjects. The system control signal for kinematics mode was the knee joint angle during FES-action. A non-linear autoregressive muscle model was built for FES action of lower limbs and its structure and parameters were determined by relating the stimulation intensity and knee joint angle in experiments. The research emphasis was laid on the control structure and algorithm of PID system modulated by artificial neural network based on feedback control signal of human kinematics information. Results showed that the relative error between the preestablished angle and the feedback signal was less than 5%. This research implied that the dual-modal information of kinetics and kinematics based on HRV and knee joint angle is quite hopeful to be the lateral stimulation trigger and feedback signal for the FES automatic control for assisted-walking and give helps to the advanced motor neuroprosthesis system design for lower limbs.更多还原