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患者出现大量功能障碍。虽然最近在假肢的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，那么高度铰接的肢体就没有什么用处。肌电信号已被证明是用于控制传统外部供电上肢假肢的有效命令源。这些商业上可获得的假体系统使用相对简单的方案，由此从承窝下方的两个部位记录的EMG信号的幅度用于致动嵌入假体中的电机之一。这允许一次仅操作单个自由度，并且需要某种类型的模式开关来在操作系统的各种接头之间转换。即使考虑到在研究竞技场中研究的肌电控制的统计模式分类的悠久历史，功能通常仍然被限制在一个单一的自由度。由于正常的人类手部功能是协调手指、拇指和手腕的同时运动，这种顺序控制方法由于显著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他们的假肢。展望未来，如果没有改进的用户界面，即使假肢的机械技术取得了相当大的进步，总体依从率也可能仍然很低。我们小组的长期目标之一是通过将完全植入的EMG记录和遥测系统用于假肢控制，为截肢者开发更多功能的肌电假肢。该提案具体涉及基于EMG的控制器的设计、测试和功能评估，该控制器使用来自残肢中的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EMG记录和模式检测算法，将有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运动时，将记录肌内EMG和手部运动学数据。人工神经网络被提出作为用于从EMG信号中的时间模式解码手指、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来直接评估拟议控制方案所促进的功能改进。通过为用户创建更透明和更轻松的控制界面，应该可以实现改善的功能结果和提高假肢的接受率。

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.