The Effect of Wrist Motion on Pattern-Recognition-Based Myoelectric Control

描述(由申请人提供)：肌电部分手假体提供了广泛的功能握手的可能性，这是以前部分手截肢患者所没有的。尽管取得了这些进展，但仍有必要开发更有效的方法来控制它们。许多有趣的研究基于模式识别的肌电控制(PRMC)的使用表明，来自手的外在肌肉的肌电信号(EMG)为高位截肢患者提供了高度准确的假肢控制。然而，这些研究未能解决部分手截肢患者所特有的一个基本挑战：手腕功能是否正常。当使用者开始移动他们的假肢时，手腕不仅可以处于任何位置，而且在移动过程中它也可以处于主动运动。此外，负责这些运动的肌肉收缩会影响外在肌肉的肌电特性，从而影响PRMC系统的性能。在这项研究中，我们将使用主成分分析来描述腕部运动学如何影响外在肌肉的肌电特性。我们将把记录的手腕运动信息整合到线性和非线性模式识别系统中。最后，我们将测试这些PRMC系统的实时能力，以适应手腕运动学的效果，保留手腕功能，改善部分手假体控制。

DESCRIPTION (provided by applicant): Myoelectric partial-hand prostheses offer the potential for a wide range of functional hand grasps not previously available to partial-hand amputees. Despite these advances, there is still an important need to develop more effective methods of controlling them. Many interesting studies investigating the use of pattern- recognition-based myoelectric control (PRMC) have shown that electromyographic signals (EMG) from the extrinsic muscles of the hand provide highly-accurate control of prostheses in individuals with high-level amputations. However, these studies fail to address an essential challenge that is unique to partial-hand amputees: the presence of a functional wrist. Not only can the wrist be in any position when the user initiates movement of their prosthesis, it can also be in active motion during the movements. Moreover, the muscle contractions responsible for these motions influence properties of the EMG from the extrinsic muscles, and consequently the performance of a PRMC system. In this study, we will use principal component analysis to describe how wrist kinematics affect the properties of EMG from the extrinsic muscles. We will incorporate recorded wrist kinematic information into both linear and non-linear pattern-recognition systems. Finally, we will test the real-time ability of these PRMC systems to accommodate the effect of wrist kinematics, preserve wrist function and improve partial-hand prosthetic control.