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

• 批准号: 8825956
• 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
• 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
• 来源: https://reporter.nih.gov/project-details/8825956
• 关键词: Accounting; Algorithms; Amputation; Beds; Bionics; Clinical; Data; Dimensions; Fingers; Freedom; Frustration; Generations; Germany; Goals; Hand; Health; Healthcare; Household; Human; Joints; Laboratories; Limb structure; Maps; Marketing; Measures; Methods; Muscle; Musculoskeletal Equilibrium; Neurosciences; Pattern Recognition; Persons; Physiologic pulse; Posture; Principal Component Analysis; Prosthesis; Psyche structure; Running; Signal Transduction; Site; System; Techniques; Testing; Thumb structure; Time; Touch sensation; United Kingdom; Upper Extremity; Veterans; Weight; Work; base; clinical practice; grasp; limb amputation; novel; prosthetic hand; residual limb; sensor; success; tool; two-dimensional

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，我们将探讨使用一种新的假肢控制器的基础上的原则，主成分分析，使无缝的姿态选择在高自由度（DOF）假手。该项目的目标是开发一个多自由度（DOF）的手假肢姿态控制器，使用肌电信号（EMG）作为控制输入，并已使用主成分分析（PCA）的尺寸优化。目前可用的多自由度手假体不能被充分利用，因为存在比待控制的自由度的数量更少的控制输入（即，欠驱动系统）。基于神经科学文献1的工作，已经表明抓握是一种“低维”任务。这项工作使用PCA来量化抓取中涉及的主成分（维度数）。结果发现，抓取任务可以很好地描述了前两个主成分。两个主成分意味着多自由度手的姿态，而把握，可以控制只有2度的控制。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 可以位于典型的经桡截肢者的残肢上。我们建议探索的优点，手姿态控制器的基础上描述的前两个主要组成部分Santello等。1和驱动使用2，3或4个肌电站点。Santello等人测量了受试者在“抓握”57件家居物品时手部的15个关节角度。由此产生的分析表明，在抓取不同物体时，关节之间存在大量的协方差。主成分分析表明，前两个主成分解释了84%的方差。这一结果表明，对于抓取任务，我们的22自由度自然手的控制减少到一个主要的二维控制问题。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的手中在姿势之间无缝移动。我们将开发一种控制算法，将肌电信号映射到Santello等人的前两个主成分的加权组合，以产生所需的姿势。Keller等人定义的所有功能性抓握，（1947）可以通过改变任一主成分的加权程度来实现。该控制器将指导只有3或4个肌电部位的高维抓握，因此使用当前可用的临床实践来控制多自由度假手。这是相关的，因为有许多新的市售手进入市场，其具有铰接的手指和多位置拇指，但是没有办法以简单的方式在抓握之间进行选择。我们将演示一个肌电驱动的PCA为基础的控制器，让它驱动一个Bebionic手2已被修改为有一个两个自由度的拇指-转换成一个6自由度的手。1 Santello，M.，弗兰德斯，M.，和Soechting J.F.，（1998）：Postural hand synergistic for tool use.神经科学杂志，18（23）10105-10115。2 RSLSteeper，罗切斯特，英国。