Adaptive neural network controller for an upper extremity neuroprosthesis
Adaptive neural network controller for an upper extremity neuroprosthesis
复制标题
用于上肢神经假体的自适应神经网络控制器
DOI：
10.1109/iembs.2004.1404153
复制
发表时间：
2004
期刊：
影响因子：
--
通讯作者：
Robert F. Kirsch
中科院分区：
文献类型：
--
作者：
Juan Gabriel Hincapie;D. Blana;Edward K. Chadwick;Robert F. Kirsch
该项目的长期目标是开发一种自适应神经网络控制器，用于针对 C5/C6 脊髓损伤 (SCI) 患者的上肢神经假体。挑战在于确定如何根据保留自主控制下肌肉的肌电图活动同时刺激不同的瘫痪肌肉。控制器从记录的肌电信号中提取运动意图，并产生适当的刺激水平来激活瘫痪的肌肉。为了测试该控制器的可行性，记录了身体健全的受试者的不同手臂运动。使用手臂的肌肉骨骼模型，逆向模拟提供了与这些运动相对应的肌肉激活模式。该模型经过修改以反映 C5/C6 SCI，并且优化标准也发生变化以反映不同的神经系统运动控制策略。然后使用激活模式来训练延时神经网络，以根据自愿肌肉激活来预测瘫痪肌肉激活。进行正向仿真以获得预测的运动，并使用运动学误差来设计自适应策略以解决系统中的干扰和变化。
The long term goal of this project is to develop an adaptive neural network controller for an upper extremity neuroprosthesis targeted for people with C5/C6 spinal cord injury (SCI). The challenge is to determine how to simultaneously stimulate different paralyzed muscles based on the EMG activity of muscles under retained voluntary control. The controller extracts the movement intention from the recorded EMG signals and generates the appropriate stimulation levels to activate the paralyzed muscles. To test the feasibility of this controller, different arm movements were recorded from able bodied subjects. Using a musculoskeletal model of the arm, inverse simulations provided muscle activation patterns corresponding to these movements. The model was modified to reflect C5/C6 SCI and the optimization criteria were varied to reflect different nervous system motor control strategies. Activation patterns were then used to train a time-delayed neural network to predict paralyzed muscle activations from voluntary muscle activations. Forward simulations were performed to obtain predicted movements and use the kinematic errors to design an adaptive strategy to account for disturbances and changes in the system.
DOI：
10.1109/86.895950
发表时间：
2000-12-01
期刊：
IEEE TRANSACTIONS ON REHABILITATION ENGINEERING
影响因子：
--
作者：
Au, ATC;Kirsch, RF
通讯作者：
Kirsch, RF