EMG-Based Multi-Joint Control of Dexterous Prosthetic Limbs

描述（由申请人提供）：上肢截肢可导致严重上肢丧失的个体的大量功能损害。虽然最近在假肢机械设计方面有了很大的发展，但如果其运动不协调或难以操作，高度铰接式的肢体几乎没有什么用处。肌电图（EMG）信号已被证明是控制常规外源性上肢假体的有效指挥来源。这些商业化的假体系统使用了一个相对简单的方案，即从插座下面的两个位置记录的肌电信号的振幅被用来驱动嵌入假体中的一个马达。这只允许一次操作一个自由度，并且需要某种类型的模式切换来在操作系统的各个关节之间进行转换。即使考虑到在研究领域研究的肌电控制的统计模式分类的悠久历史，功能通常仍然局限于一个单一的自由度。由于正常的人的手的功能是协调的，同时运动的手指，拇指和手腕，这种顺序控制方法可能是令人沮丧的缓慢由于显著的认知负担；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，即使假肢在机械方面取得了相当大的进步，总体顺应率也可能仍然很低。我们团队的长期目标之一是为截肢者开发功能更强的肌电假肢，通过整合一个完全植入的肌电记录和遥测系统来控制假肢。本提案专门针对基于肌电图的控制器的设计、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。总体假设是，通过使用4-8个肌内肌电图记录和模式检测算法，可以以高度协调和同时的方式控制肌电假体的多个关节。当受试者进行各种需要不同抓握模式的运动时，将记录肌肉内肌电图和手部运动数据。人工神经网络是一种从肌电信号的时间模式中解码手指、手腕和拇指运动轨迹的方法。通过对现实任务的虚拟现实模拟，将对拟议控制方案所促进的功能改进进行直接评估。通过为用户创建一个更透明、更轻松的控制界面，可以改善假肢的功能结果，提高假肢的接受率。

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. PUBLIC HEALTH RELEVANCE: 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, there is still a need for more intuitive user interfaces. This study aims to demonstrate natural and simultaneous control of a dexterous prosthetic hand and wrist using patterns in the electrical activity generated during muscle contraction.