EMG-Based Multi-Joint Control of Dexterous Prosthetic Limbs

描述(申请人提供)：上肢截肢可导致严重上肢丧失患者的大量功能损害。虽然假肢的机械设计最近有了很大的发展，但如果肢体的运动协调不好或难以操作，高度关节的肢体就没有什么用处。肌电(EMG)信号已被证明是控制传统外力上肢假体的有效指令源。这些商业上可用的假体系统使用相对简单的方案，由此从插座下方的两个位置记录的EMG信号的幅度被用来驱动嵌入假体中的一个马达。这使得一次只能操作一个自由度，并且需要某种类型的模式开关来在操作系统的各个关节之间进行转换。即使考虑到肌电控制的统计模式分类在研究领域的悠久历史，功能通常仍然局限于一次单一自由度。由于人类正常的手功能是手指、拇指和手腕同时协调运动，这种顺序控制方法可能会因为巨大的认知负担而缓慢得令人沮丧；这导致大多数截肢者选择长期不使用假肢。展望未来，如果没有改进的用户界面，即使假肢有了相当大的机械进步，总体遵从率也可能保持在较低水平。我们团队的长期目标之一是通过整合用于假体控制的完全植入的肌电记录和遥测系统，为截肢者开发更具功能性的肌电假体。这项建议专门针对基于肌电的控制器的设计、测试和功能评估，该控制器使用来自残肢肌肉的信息来识别用户的整体、多关节运动意图。最重要的假设是，通过使用4-8个肌肉内肌电记录和模式检测算法，将可能允许以高度协调和同时的方式控制肌电假体的多个关节。当受试者执行需要不同抓握模式的各种动作时，肌肉内肌电和手部运动学数据将被记录下来。人工神经网络被提出作为一种从EMG信号的时间模式中解码手指、手腕和拇指的运动轨迹的方法。将通过对现实任务的虚拟现实模拟，对拟议控制方案促进的功能改进进行直接评估。通过为用户创建更透明和更轻松的控制界面，应该可以改善功能结果和增加假肢的接受率。

DESCRIPTION (provided by applicant): Upper extremity amputations can cause a great deal of functional impairment in individuals living with major upper limb loss. While there has been a great deal of recent development in the mechanical design of prosthetic arms, a highly articulated limb is of little use if its movements are not well coordinated or if it is difficult to operate. Electromyographic (EMG) signals have proven to be effective command sources for control of conventional externally-powered upper limb prostheses. These commercially available prosthetic systems use a relatively simple scheme, whereby the amplitude of EMG signals recorded from two sites beneath the socket are used to actuate one of the motors embedded in the prosthesis. This allows only a single degree of freedom to be operated at a time, and requires some type of a mode switch to transition between operating the various joints of the system. Even considering the long history of statistical pattern classification for myoelectric control studied in the research arena, function is usually still limited to a single degree of freedom at a time. Since normal human hand function has coordinated, simultaneous movement of the fingers, thumb, and wrist, this sequential control method can be frustratingly slow due to the significant cognitive burden; this has resulted in the majority of amputees choosing to not use their prostheses over the long term. Going forward, without an improved user interface the overall compliance rate may remain low even with the considerable mechanical advances in prosthetic limbs. One of the long-term goals of our group is to develop more functional myoelectric prostheses for amputees by incorporating a fully implanted EMG recording and telemetry system for use in prosthesis control. This proposal specifically addresses the design, testing, and functional evaluation of EMG-based controllers that use information from muscles in the residual limb to identify the overall, multi-joint motion intent of the user. The overarching hypothesis is that by using 4-8 intramuscular EMG recordings and a pattern detection algorithm, it will be possible to allow multiple joints of a myoelectric prosthesis to be controlled in a highly coordinated and simultaneous fashion. Intramuscular EMG and hand kinematic data will be recorded as subjects perform a variety of movements that require different grasp patterns. Artificial neural networks are proposed as a method for decoding the movement trajectories of the fingers, wrist, and thumb from temporal patterns in the EMG signals. A direct assessment of the functional improvements facilitated by the proposed control scheme will be performed via virtual reality simulations of realistic tasks. By creating a more transparent and effortless control interface for the user, improved functional outcomes and increased acceptance rates of prosthetic limbs should be achievable.