自主意愿控制的运动功能康复功能性电刺激技术基础研究

The motor function of patients who suffer from stroke and spinal cord injury have decreased or even lost, because the motor nerve signal transmission was interrupted by the injury. Based on the plasticity of the central nervous system theory, it will help to motivate and strengthen the recovery of the motion function of patients after stroke if the volitional will of patients is integrated into the functional electrical stimulation (FES) therapy. Inspired by this rehabilitation therapy theory, based on the structure and function of neuromuscular system, a dynamics model of electrically stimulated skeletal muscle is firstly investigated. In the modelling process, the uncertainty of skeletal muscle system and the external disturbance was analyzed in detail, and the parameters uncertainty, time varying, nonlinearity and randomness was also considered. In order to use the volitional electromyography (EMG) as a control signal for the stimulation of the same muscle, it is necessary to eliminate the stimulation artifacts and the muscle response artifacts caused by the stimulation. So secondly, considering the uncertain parameters specially, the robust filtering algorithm was studied to recognize the volitional will. Thirdly, the precise close-loop control of FES system was designed, and the volitional electromyography expressing the volitional will of patient was integrated into the functional electrical stimulation system in two levels to control electrical stimulation output. Finally, rehabilitation training of upper limb of stroke patients was tested. The dynamics model of electrically stimulated skeletal muscle and the extracting method of volitional EMG was studied comprehensively in the project, providing an intelligent rehabilitation technology to promote the rehabilitating effect. It is significant both in academic research and medical applications.

脑卒中与脊髓损伤患者由于神经损伤导致运动神经信号无法传递而中断，运动功能下降甚至丧失。基于中枢神经系统的可塑性理论，在功能性电刺激治疗中融入患者的自主意愿，对运动功能恢复有着激发和强化作用。受此康复治疗理论启发，本项目首先基于神经肌肉系统结构和功能机理，分析骨骼肌系统不确定性及外界扰动来源，考虑存在的参数不确定性、时变性、非线性特性及随机性等因素，建立电刺激下骨骼肌动态过程模型；其次研究适合模型参数具有不确定性的鲁棒滤波算法，从无创的表面肌电信号中滤除电刺激伪迹与肌肉反应伪迹，识别自主意愿；接下来设计精确控制的功能性电刺激闭环系统，将主动意愿融入功能性电刺激系统中，利用主动意愿控制电刺激输出；最后进行上肢运动功能康复训练实验。项目系统研究了电刺激下骨骼肌动态过程及基于肌电信号的自主意愿提取方法，为临床提供一种智能康复技术，从而提升运动功能康复效果，项目研究具有重要的理论意义与医学应用价值。

功能性电刺激利用低频电流刺激失去神经控制的肌肉，使其收缩，以替代或矫正器官或肢体丧失的功能。目前在临床上已广泛应用于上运动神经元损伤，如脑卒中、脑外伤、脊髓损伤等导致的肢体运动功能障碍康复，但目前大部分的功能性电刺激治疗都是以被动治疗为主，制约了康复效果，降低了康复过程的安全性。本项目基于表面肌电信号进行患者运动意图识别，充分研究了自主意愿控制的功能性电刺激控制系统的神经肌肉骨骼建模、控制与分析、应用与评估，建立了较为完整的基于自主意愿控制的功能性电刺激的方法与系统。本项目的研究取得了如下成果：.1. 解决了功能性电刺激系统下的电刺激伪迹滤除问题，建立了电刺激时神经肌肉骨骼系统的参数模型和非参数概率模型，分析了电刺激下神经肌肉骨骼系统的机理特性，为智能康复系统的研究和设计提供理论依据和模型基础。.2. 建立了基于肌电和脑电信号较为完整的运动意图识别理论体系与方法。分别基于机理和人工智能方法开展离散和连续运动意图解码研究。研究从动态肌电和低频肌电信号解码、跨用户和力度无关鲁棒特征等方面着手解决实时性和实用性问题，以易测量的关节角度为输出构成闭环扩展卡尔曼滤波器实现力矩预测，解决力矩预测值的漂移。最后扩展研究基于脑电信号的运动想象解码。.3. 研究基于患者主动意愿控制的功能性电刺激技术，以运动参数为反馈，完成了基于自主意愿控制的功能性电刺激闭环和镜像系统的仿真与实验，包括上肢肘关节和下肢足下垂研究。.4. 研制功能性电刺激样机，进行脑卒中和脊髓损伤患者康复训练，验证该技术疗效。.5. 发表国内外期刊论文15篇，会议论文2篇，其中SCI收录8篇（中科院二区及以上6篇），EI收录5篇。授权国家发明专利3项，实用新型专利4项，软件著作权1项。培养博士生3名，硕士生11名。.本项目的研究具有重要的理论意义，促进了多学科交叉的发展，并为临床提供一种智能康复技术，有助于患者神经系统的重塑，加速患者的受损功能康复，使患者能尽快重归社会，产生了较好的社会影响和应用价值。

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