Simultaneous Control of Multiple Degrees of Freedom in Myoelectric Hand Prostheses (SimCon)

该项目的目的是开发一种全新的生物控制方法，使上肢假体中的多个关节能够同时控制。任何肢体的丧失，尤其是手的丧失，都会深刻地影响一个人的生活质量。先进的假肢可以为截肢者提供功能康复的途径，使他们能够进行日常活动，提高他们重返职业和赚取正常生计的机会。关于使用假手的调查显示，20%的截肢者放弃他们的假肢，一个关键原因是它不能提供足够的功能。因此，提高上肢假肢的性能是迫切需要的。Reinhold Reiter于1945年在一项专利申请中披露的开-关1自由度控制范式仍然广泛用于假肢控制。早在1967年，Finley就表明开关控制不能给使用者提供足够的灵活性，并提出使用模式识别通过处理肌肉的电活动，即肌电图或肌电信号来估计义肢使用者的运动意图。今天，芬利的提议已经过去了50年，尽管实验室已经进行了出色的演示，但将肌电假肢的模式识别商业化仍然不可行，因为1)截肢者很难为不同的运动类别产生不同的活动模式；2)由于电极位移和残肢运动，模式识别性能经常会下降。在这个项目中，我们将首先探索手部和前臂的肌肉在多大程度上可以学习产生新的共同收缩模式（又名肌肉协同作用），因为自然协同作用可能会因截肢而中断。从这项实验工作中获得的见解将为设计新的算法提供信息，以同时控制多个关节（自由度）的运动。当用户与假体交互时，这些算法可以自我调整，以提高性能。该项目将在临床前试验中达到高潮，其中四名截肢者将用假肢测试原型控制算法。将所提出的范例的性能与传统的假体开关控制方法进行比较。提出的研究项目将产生一种同时控制多个自由度的有效方法。这为用户提供了比当前基于开关或模式识别的控制方法更大的灵活性。实验结果表明，该方法可以在100ms内响应用户的运动意图，比目前最先进的上肢假肢控制速度快至少3倍。这项研究的结果将为未来几代“即插即用”的假肢铺平道路，这些假肢具有“随时可用”和“与佩戴者无关”的特点。

The aim of this project is to develop a radically novel and biologically-informed control approach that enables simultaneous control of multiple joints in an upper-limb prosthesis.The loss of any limb, particularly the hand, affects an individual's quality of life profoundly. Advanced prosthetic hands can provide a route to functional rehabilitation by allowing the amputees to undertake their daily activities and improve their chances of returning to their careers and earning their regular livelihood. Surveys on the use of artificial hands reveal that 20% of the amputees abandon their prosthesis with a key reason being that it does not provide enough function. Therefore, performance enhancement of upper-limb prostheses is a pressing need.The on-off 1-degree of freedom control paradigm that Reinhold Reiter disclosed in a patent application in 1945 is still used widely for prosthesis control. As early as 1967, Finley showed that the on-off control does not offer enough flexibility to the user and proposed the use of pattern recognition to estimate prosthesis user's movement intention by processing electrical activity of muscles, known as the electromyogram or myoelectric signals. Today, 50 years after Finley's proposal and despite remarkable laboratory demonstrations, it has not been feasible to commercialise pattern recognition in a myoelectric prosthesis hand because 1) it is very difficult for the amputees to generate distinct activity patterns for different movement classes and 2) pattern recognition performance often deteriorates due to electrode displacement and movement of the residual limb.In this project, we will firstly explore the extent to which muscles in the hand and forearm can learn to generate novel co-contraction patterns (aka muscle synergies) because natural synergies may be disrupted by amputation. The insight gained from this experimental work will inform design of novel algorithms to enable simultaneous control of multiple joints (degrees of freedom) movements. These algorithms can self-tune, to improve performance, as the user interacts with the prosthesis. The project will culminate in a pre-clinical trial in which four amputee subjects test the prototyped control algorithm with a prosthesis. The performance of the proposed paradigm will be compared to that of the conventional prosthesis on-off control method. The proposed research project will produce an efficient approach for simultaneous control of multiple degrees of freedom. This offers the user much greater flexibility than current on-off or pattern recognition-based control approaches. Pilot results show that with this approach the prosthesis can respond to user's motion intention in only 100ms, which is at least three times faster than the state-of-the-art in upper-limb prostheses control. Results of this research will pave the way for future generations of "plug and play" prostheses with "ready-to-go" and "wearer-independent" features.