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

肌电假手多个自由度的同时控制 (SimCon)

• 批准号: EP/M025594/1
• 负责人: Kianoush Nazarpour
• 金额: $ 12.78万
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FM025594%2F1
• 关键词: Simultaneous; Control; Multiple; Degrees; Freedom

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.

该项目的目的是开发一种全新的生物学控制方法，可以同时控制上肢假肢中的多个关节。任何肢体的丧失，特别是手，都会深刻影响个人的生活质量。先进的假手可以提供一条功能康复的途径，使被截肢者能够进行日常活动，并增加他们重返职业生涯和赚取正常生计的机会。关于假手使用情况的调查显示，20%的截肢者放弃假肢，一个关键原因是它不能提供足够的功能。Reinhold Reiter在1945年的专利申请中公开的开关式单自由度控制范例仍然被广泛用于假肢控制。早在1967年，Finley就指出开-关控制不能为用户提供足够的灵活性，并提出使用模式识别来通过处理肌肉的电活动（称为肌电图或肌电信号）来估计假肢用户的运动意图。今天，在Finley的提议50年后，尽管有显著的实验室示范，但在肌电假手中商业化模式识别还不可行，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）由于电极移位和残肢的运动，模式识别性能经常恶化。我们将首先探索手部和前臂的肌肉可以学习产生新的共同收缩模式（又名肌肉协同作用）的程度，因为截肢可能会破坏自然的协同作用。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，以实现多个关节（自由度）运动的同时控制。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，在该试验中，四名截肢受试者使用假肢测试原型控制算法。所提出的范例的性能进行比较，传统的假体开关控制方法。拟议的研究项目将产生一个有效的方法，同时控制多个自由度。这为用户提供了比当前的开-关或基于模式识别的控制方法更大的灵活性。初步结果表明，该方法的假肢可以响应用户的运动意图，只有100毫秒，这是至少三倍的速度比最先进的上肢假肢控制。这项研究的结果将为未来几代具有“随时可用”和“佩戴者独立”功能的“即插即用”假肢铺平道路。

项目成果

Processing occlusions using elastic-net hierarchical MAX model of the visual cortex

使用视觉皮层的弹性网络分层 MAX 模型处理遮挡

• DOI: 10.1109/inista.2017.8001150
• 发表时间: 2017
• 作者: Alameer A

Objects and scenes classification with selective use of central and peripheral image content

• DOI: 10.1016/j.jvcir.2019.102698
• 发表时间: 2020-01-01
• 期刊: JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
• 影响因子: 2.6
• 作者: Alameer, Ali;Degenaar, Patrick;Nazarpour, Kianoush
• 通讯作者: Nazarpour, Kianoush

Improvement in modelling of physiological tremor by inclusion of grip force in quaternion weighted Fourier linear combiner

• DOI: 10.1049/cp.2015.1782
• 发表时间: 2015
• 作者: K. Adhikari;S. Tatinati;K. Veluvolu;K. Nazarpour

An elastic net-regularized HMAX model of visual processing

• DOI: 10.1049/cp.2015.1753
• 发表时间: 2015
• 作者: Ali Alameer;G. Ghazaeil;P. Degenaar;K. Nazarpour

Incoherent Dictionary Pair Learning: Application to a Novel Open-Source Database of Chinese Numbers

• DOI: 10.1109/lsp.2018.2798406
• 发表时间: 2018-01
• 期刊: IEEE Signal Processing Letters
• 影响因子: 3.9
• 作者: V. Abolghasemi;Mingyang Chen;Ali Alameer;S. Ferdowsi;Jonathon A. Chambers;K. Nazarpour