Semiparametric Identification of Human Arm Dynamics for Flexible Control of a Functional Electrical Stimulation Neuroprosthesis.
Semiparametric Identification of Human Arm Dynamics for Flexible Control of a Functional Electrical Stimulation Neuroprosthesis.
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DOI：
10.1109/tnsre.2016.2535348
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发表时间：
2016-12
期刊：
影响因子：
--
通讯作者：
Lynch KM
中科院分区：
文献类型：
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作者：
Schearer EM;Liao YW;Perreault EJ;Tresch MC;Memberg WD;Kirsch RF;Lynch KM
我们提出了一种方法来识别由植入的功能性电刺激神经假体控制的人臂的动力学。该方法使用高斯过程回归来预测肩部和肘部扭矩，给定肩部和肘部关节位置和速度以及肌肉的电刺激输入。我们比较了非参数、半参数和参数模型类型的扭矩预测的准确性。三种模型类型中最准确的是半参数高斯过程模型，它结合了黑盒函数逼近器的灵活性和参数化模型的泛化能力。半参数模型预测刺激多个肌肉期间的扭矩，其误差小于总肌肉扭矩和驱动手臂所需的被动扭矩的 20%。确定的模型使我们能够定义任意到达轨迹，并大致确定沿着该轨迹驱动手臂所需的肌肉刺激。
We present a method to identify the dynamics of a human arm controlled by an implanted functional electrical stimulation neuroprosthesis. The method uses Gaussian process regression to predict shoulder and elbow torques given the shoulder and elbow joint positions and velocities and the electrical stimulation inputs to muscles. We compare the accuracy of torque predictions of nonparametric, semiparametric, and parametric model types. The most accurate of the three model types is a semiparametric Gaussian process model that combines the flexibility of a black box function approximator with the generalization power of a parameterized model. The semiparametric model predicted torques during stimulation of multiple muscles with errors less than 20% of the total muscle torque and passive torque needed to drive the arm. The identified model allows us to define an arbitrary reaching trajectory and approximately determine the muscle stimulations required to drive the arm along that trajectory.