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

手腕运动对基于模式识别的肌电控制的影响

• 批准号: 8598223
• 负责人: Adenike A Adewuyi
• 金额: $ 4.72万
• 依托单位: NORTHWESTERN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-12-01 至 2017-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8598223
• 关键词: Accounting; Achievement; Activities of Daily Living; Address; Affect; Amputation; Amputees; Data; Devices; Digit structure; Fingers; Goals; Hand; Human; Individual; Joints; Limb structure; Methods; Motion; Movement; Muscle; Muscle Contraction; Outcome; Pattern Recognition; Pattern Recognition Systems; Performance; Positioning Attribute; Principal Component Analysis; Property; Prosthesis; Research; Residual state; Signal Transduction; System; Technology; Testing; Time; Upper Extremity; Variant; Wrist; base; clinical application; functional loss; hand grasp; improved functioning; interest; kinematics; limb amputation; performance tests; public health relevance; vector; virtual

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.

描述（由申请人提供）：肌电部分手假肢提供了以前部分手截肢者无法获得的各种功能性手抓握的可能性。尽管取得了这些进展，但仍然迫切需要开发更有效的控制方法。许多有趣的研究使用基于模式识别的肌电控制（PRMC）已经表明，来自手部外部肌肉的肌电图信号（EMG）为高位截肢患者的假肢提供了高度精确的控制。然而，这些研究未能解决部分手截肢者所特有的一个基本挑战：功能性手腕的存在。当使用者启动其假肢的运动时，手腕不仅可以处于任何位置，而且还可以在运动期间处于主动运动中。此外，负责这些运动的肌肉收缩影响来自外在肌肉的EMG的性质，并且因此影响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学如何影响来自外部肌肉的肌电信号的属性。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，提高部分手假肢控制。