Myoelectric signal source separation for prosthetic control

用于假肢控制的肌电信号源分离

• 批准号: 345026-2007
• 负责人: Englehart, Kevin
• 金额: $ 6.24万
• 依托单位: University of New Brunswick
• 依托单位国家: 加拿大
• 项目类别: Research Tools and Instruments - Category 1 (<$150,000)
• 财政年份: 2006
• 资助国家: 加拿大
• 起止时间: 2006-01-01 至 2007-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=326158
• 关键词: Myoelectric; signal; source; separation; prosthetic

This research focuses on the functional replacement of the wrist and hand, in the context of an individual who has an amputation (or congenital deficiency) below the elbow.  This represents almost two-thirds of the amputee population.   For those with upper limb deficiencies, currently available prosthetic limbs offer limited function.  This is due, in large part, to the lack of control information that can be delivered to the device by the user.  Significant advances have been made in recent years in interpreting a user's intent by monitoring the myoelectric signal at multiple sites, and using advanced signal processing to determine the intended motion.  The applicants have demonstrated that it is possible to control wrist and hand function with high reliability using a pattern recognition approach.   Although this represents a significant advance, pattern recognition allows only one function to be actuated at a given time.  This requires a user to manipulate one joint at a time; if a task requires multiple joints, these must be done in sequence.  Clearly this is not how one uses an intact limb.  This research promises simultaneous and independent control of multiple joints to deliver natural, coordinated motion to prosthetic devices.  This will be accomplished using a combination of source separation methods and inverse modeling to resolve the activity the forearm muscles.  By determining the level of activity of individual muscles which act as the "prime movers" in hand and wrist manipulation, each joint may be controlled independently. The ability to control hand and wrist function in a natural, coordinated manner will represent a tremendous enhancement in prosthetic control.

这项研究的重点是手腕和手的功能置换，在肘部以下截肢(或先天性缺陷)的个人的情况下。这几乎占截肢者人口的三分之二。对于那些上肢缺陷的人来说，目前可用的假肢提供的功能有限。这在很大程度上是由于用户缺乏可以传递给设备的控制信息。近年来，通过监测多个部位的肌电信号来解释用户的意图方面取得了重大进展，并使用先进的信号处理来确定预期的运动。申请人已经证明，使用模式识别方法可以高可靠性地控制手腕和手的功能。虽然这是一个重大的进步，但模式识别在给定的时间只允许一种功能被激活。这需要用户一次操作一个关节；如果一项任务需要多个关节，这些必须按顺序完成。显然，这不是一个人使用完整肢体的方式。这项研究承诺同时独立控制多个关节，为假肢设备提供自然、协调的运动。这将使用源分离方法和反向建模相结合的方法来解决前臂肌肉的活动。通过确定在手和手腕操作中充当“主要推动者”的单个肌肉的活动水平，每个关节都可以被独立控制。以自然、协调的方式控制手和手腕功能的能力将代表着假体控制的巨大增强。