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，我们将探索使用基于主成分分析原理的新型假肢控制器，以实现高自由度（DOF）假手的无缝姿势选择。该项目的目标是开发一种多自由度 (DOF) 手假肢姿势控制器，该控制器使用肌电信号 (EMG) 作为控制输入，并使用主成分分析 (PCA) 进行尺寸优化。目前可用的多自由度手假肢无法充分利用，因为控制输入少于要控制的自由度数量（即欠驱动系统）。基于神经科学文献1的研究表明，抓取是一项“低维”任务。这项工作使用 PCA 来量化抓取过程中涉及的主成分（维度数）。研究发现，前两个主要成分可以很好地描述抓取任务。两个主成分意味着多自由度手在抓握时的姿势可以仅使用 2 个控制度来控制。这是一个令人鼓舞的发现，因为目前的临床上肢假肢实践表明只有 3 或 4 个独立的肌电部位可以位于典型经桡动脉截肢者的残肢上。我们建议探索基于 Santello 等人 1 描述的前两个主要组件并使用 2、3 或 4 个肌电点驱动的手势控制器的优点。桑特洛等人。在“抓握”57 个家用物品时，测量了受试者手中的 15 个关节角度。结果分析显示，在抓取不同物体时，关节之间存在大量协方差。主成分分析表明，前两个主成分占方差的 84%。这一结果表明，对于抓取任务，我们的 22 DOF 自然手的控制很大程度上减少为二维控制问题。将这一发现应用于多关节假手的控制意味着我们可以使用 2-4 个肌电极，但仍然能够在多自由度手的姿势之间无缝移动。我们将开发一种控制算法，将肌电信号映射到 Santello 等人的前两个主要成分的加权组合，以产生所需的姿势。 Keller 等人 (1947) 定义的所有功能把握都可以通过改变任一主成分的加权程度来实现。该控制器将仅用 3 或 4 个肌电点来指导高维抓取，从而利用当前可用的临床实践来控制多自由度假手。这是相关的，因为市场上出现了许多带有铰接手指和多位置拇指的新型市售手，但无法以简单的方式在抓握之间进行选择。我们将通过驱动 Bebionic Hand2 来演示 EMG 驱动的基于 PCA 的控制器，该控制器已被修改为具有两个自由度拇指 - 将其转换为 6 DOF 手。 1 Santello, M.、Flanders, M. 和 Soechting J.F., (1998)：工具使用的手姿势协同作用。神经科学杂志，18(23)10105-10115。 2 RLSteep，英国罗切斯特。