Nonlinear Filtering of Athetoid Movement

描述（由申请人提供）：本提案的目标是开发动脉状突运动的非线性模型，并将这些模型用于过滤算法，以帮助动脉状突脑瘫（CP）患者的计算机访问。对于患有脑瘫和其他疾病的人来说，动脉粥样硬化是电脑使用的主要障碍（因此也影响了一般的独立性）。这是一种复杂的运动障碍，研究已经确定了不自主运动的多种成分，包括准周期性（即震颤）和非周期性成分。线性滤波的好处是有限的，因为徐动症既减少了有目的运动的带宽，又破坏了在相同频率下的非自愿运动的剩余带宽。在本研究中，将开发一种非线性方法来估计动脉粥样硬化患者的预期计算机输入。研究将进行如下工作：1。患有手足状脑瘫的人类受试者将使用操纵杆执行一系列计算机任务，而不需要任何输入过滤。受试者将使用一种新型的等距操纵杆完成一系列目标获取或“点击图标”的任务，这种操纵杆非常适合于动脉状突使用者。操纵杆读数和屏幕光标位置将被记录下来。2. 在这些实验中，神经网络将被用于开发预期和实际运动之间映射的非线性黑盒模型。一个通用的神经网络结构，级联-相关学习架构，将用于建模。3. 开发的模型将使用记录的数据进行验证。验证将使用非常适合评估人类表现建模数据的随机相似性度量来执行。4. 经过验证的模型将作为输入过滤器在线执行，然后由动脉粥样硬化受试者在现实的计算机任务中进行现场测试。受试者将完成带过滤和不带过滤的目标获取试验，并对性能结果进行统计比较。

DESCRIPTION (provided by applicant): The goal of this proposal is to develop nonlinear models of athetoid movement and to use these models in filtering algorithms to assist computer access for persons with athetoid cerebral palsy (CP). Athetosis is a major barrier to computer usage (and therefore to general independence) for persons with cerebral palsy and other diseases. It is a complex movement disorder in which studies have identified multiple components of involuntary motion, including both quasi-periodic (i.e., tremor) and aperiodic components. Linear filtering is of limited benefit, since athetosis both reduces the bandwidth of purposeful movement and corrupts the remaining bandwidth with involuntary movement at the same frequencies. In this research, a nonlinear method for estimating intended computer input by persons with athetosis will be developed. The research will proceed as follows: 1. Human subjects with athetoid CP will use a joystick to execute a series of computer tasks without any input filtering. Subjects will complete a series of target acquisition or "icon-clicking" tasks using a novel isometric joystick that is well suited for athetoid users. The joystick readings and screen cursor positions will be recorded. 2. Neural networks will be used to develop nonlinear black-box models of the mapping between intended and actual movement during these experiments. A versatile neural network structure, the cascade-correlation learning architecture, will be used for modeling. 3. The models developed will be validated using recorded data. Validation will be performed using a stochastic similarity measure that is well suited to evaluation of human performance modeling data. 4. The validated models will be implemented online as input filters and will then be field- tested by the athetoid subjects in realistic computer tasks. Subjects will complete target acquisition trials both with and without filtering, and the performance results will be compared statistically.