A Postural Control Paradigm for EMG Control of Advanced Prosthetic Hands

先进假手肌电图控制的姿势控制范例

基本信息

批准号：
9000726
负责人：
RICHARD Fergus ffrench WEIR
金额：
--
依托单位：
VA EASTERN COLORADO HEALTH CARE SYSTEM
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-01-01 至 2016-12-31
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): In this project we will explore the use of a novel prosthesis controller based on the principle of Principal Component Analysis to enable seamless posture selection in high degree-of-freedom (DOF) prosthetic hands. The goal of this project is to develop a multi-degree of freedom (DOF) hand prosthesis posture controller that uses myoelectric signals (EMG) as control inputs and which has been dimensionally optimized using principal component analysis (PCA). Currently available multi-DOF hand prostheses cannot be fully utilized because there are fewer control inputs than the number of DOFs to be controlled (i.e. an underactuated system). Based on work from the neuroscience literature1 it has been shown that grasping is a 'low dimensional' task. This work used PCA to quantify the principal components (number of dimensions) involved in grasping. It was found that grasping tasks could be well described by the first two principal components. Two principal components implies that the posture of a multi-DOF hand, while grasping, can be controlled using only 2 degrees-of-control. This is an encouraging finding since current clinical upper limb prosthetic practice indicates only 3 or 4 independent myoelectric sites can be located on the residual limb of a typical person with a transradial amputation. We propose to explore the merits of a hand posture controller based on the first two principal components described by Santello et al.1 and driven using 2, 3 or 4 myoelectric sites. Santello et al. measured 15 joint angles in the hand of the subjects while 'grasping' 57 household objects. The resulting analysis showed a high amount of covariance between the joints while grasping different objects. A principal component analysis showed that the first two principal components accounted for 84% of the variance. This result suggests that, for grasping tasks, control of our 22 DOF natural hand reduces to a largely 2 dimensional control problem. Applying this finding to the control of multi-articulated prosthetic hands means we can use 2-4 myoelectrodes yet still be able to seamlessly move between postures in a multi-DOF hand. We will develop a control algorithm that will map the myoelectric signals to weighted combinations of Santello et al.s first two principal components to yield a desired posture. All functional grasp as defined by Keller et al., (1947) are achievable by varying the degree to which either principal component is weighted. This controller will direct high-dimension grasps with only 3 or 4 myoelectric sites and therefore control a multi-degree of freedom prosthetic hand using currently available clinical practices. This is of relevance because there are a number of new commercially available hands coming onto the market with articulated fingers and multi- positional thumbs - but with no way to select between grasps in a easy manner. We will demonstrate an EMG- driven PCA-based controller by having it drive a Bebionic Hand2 which has been modified to have a two degree-of-freedom thumb - converting it into a 6 DOF hand. 1 Santello, M., Flanders, M., and Soechting J.F., (1998): Postural hand synergies for tool use. J. Neuroscience, 18(23)10105-10115. 2 RSLSteeper, Rochester, United Kingdom.

描述（由申请人提供）：在这个项目中，我们将根据主要成分分析的原则探索新型假体控制器的使用，以使高自由度（DOF）假体的无缝姿势选择。该项目的目的是开发使用肌电信号（EMG）作为控制输入的多度自由（DOF）手提假体姿势控制器，并使用主组件分析（PCA）在尺寸上优化了尺寸。当前可用的多道型手工假肢无法得到充分利用，因为控制输入少于要控制的DOF的数量（即一个不足的系统）。基于神经科学文献的工作1，已经表明抓握是“低维”任务。这项工作使用PCA来量化掌握涉及的主要成分（尺寸数）。发现前两个主要组件可以很好地描述掌握任务。两个主要成分意味着，在抓住的同时，只能使用2个控制程度来控制多道手的姿势。这是一个令人鼓舞的发现可以位于典型人的残留肢体上，具有跨性别截肢。我们建议根据Santello等人1所描述的前两个主要组件探索手姿势控制器的优点，并使用2、3或4个肌电位点驱动。 Santello等。在受试者的手中测量了15个关节角，同时“抓住” 57个家用物体。最终的分析显示，关节之间的协方差很高，同时抓住了不同的物体。主成分分析表明，前两个主要组件占方差的84％。该结果表明，对于掌握任务，控制我们的22个DOF自然手的控制将减少到大多数2维控制问题。将此发现应用于控制多个假肢手的控制，这意味着我们可以使用2-4个肌电型，但仍然能够在多道手中的姿势之间无缝移动。我们将开发一种对照算法，该算法将将肌电信号映射到Santello等人的加权组合，以产生所需的姿势。 Keller等人（1947年）所定义的所有功能掌握都可以通过改变对任一主成分加权的程度来实现。该控制器将仅使用3或4个肌电位点指导高维度的抓地力，因此使用当前可用的临床实践来控制多度自由假体手。这是相关的，因为有许多新的市售手伸出了手指和多位置拇指 - 但无法以一种简单的方式在抓地力之间进行选择。我们将通过使其驱动Bebionic Hand2进行修改以具有两个自由度的拇指 - 将其转换为6 DOF手，从而证明了基于PCA的控制器。 1 Santello，M.，Flanders，M。和Soechting J.F.（1998）：用于工具使用的姿势手工协同作用。 J. Neuroscience，18（23）10105-10115。 2 Rslsteeper，英国罗切斯特。