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

描述（由申请人提供）：肌电部分手假肢提供了广泛的手部握住功能的潜力，这是以前部分手截肢者无法获得的。尽管取得了这些进展，但仍然需要开发更有效的方法来控制它们。许多有趣的研究调查了基于模式识别的肌电控制（PRMC）的使用，表明来自手部外部肌肉的肌电信号（EMG）为高位截肢患者提供了高度精确的假肢控制。然而，这些研究未能解决部分手部截肢者特有的一个基本挑战：功能性手腕的存在。当用户启动假肢运动时，手腕不仅可以处于任何位置，还可以在运动过程中处于主动运动状态。此外，负责这些运动的肌肉收缩影响来自外部肌肉的肌电图特性，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手腕运动如何影响来自外部肌肉的肌电图特性。我们将把记录的手腕运动信息纳入线性和非线性模式识别系统。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影响，保持手腕功能和改善部分手假肢的控制。

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.