Sensorimotor Learning for Control of Prosthetic Limbs

用于控制假肢的感觉运动学习

• 批准号: EP/R004242/1
• 负责人: Kianoush Nazarpour
• 金额: $ 131.07万
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FR004242%2F1
• 关键词: Sensorimotor; Learning; Control; Prosthetic; Limbs

Worldwide, there are over three million people living with upper-limb loss. Recent wars, industrialisation in developing countries and vascular disease, e.g. diabetes, have caused the number of amputations to soar. Adding to this population each year, one in every 2,500 people are born with upper-limb reduction. Advanced prostheses can play a major role in enhancing the quality of life for people with upper-limb loss, however, they are not available under the NHS. Notably, many people with traumatic limb loss are otherwise physically fit. If they are equipped with advanced prostheses and treated to recover psychologically, they can live independently, with minimal need for social support, return to work and contribute to the economy. There are a plethora of underlying reasons that limit wide clinical adoption of advanced prosthetic hands. For instance, surveys on their use reveal that 20% of upper-limb amputees abandon their prosthesis, with the primary reason being that the control of these systems is still limited to one or two movements. In addition, the process of switching a prosthetic hand into an appropriate grip mode, e.g. to use scissors, is cumbersome or requires an ad-hoc solution, such as using a smart phone application. Other reasons include: users finding their prosthesis uncomfortable or unsuitable for their needs. As such, everyday tasks, such as tying shoe-laces, are currently very challenging for prosthetic hand users. These functional shortcomings, coupled with high costs and lack of concrete evidence for added benefit, have emerged as substantial barriers limiting clinical adoption of advanced prosthetic hands.The long-term aim of this cross-disciplinary programme is to develop, and move towards making available, the next generation of prosthetic hands that can improve the users' quality of life. Our underlying scientific novelty is in utilising users' capability of learning to operate a prosthesis. For instance, we examine the extent to which the activity of muscles can deviate from natural patterns employed in controlling movement of the biological arm and hand and whether prosthesis users can learn to synthesise these functional maps between muscles and prosthetic digits. Basing this approach upon our pilot data, we hypothesise that practice and availability of sensory feedback can accelerate this learning experience. To address this fundamental question, we will employ in-vivo experiments, exploratory studies involving able-bodied volunteers and pre-clinical work with people with limb loss. The insight gained from these studies will inform the design of novel algorithms to enable seamless control of prosthetic hands. Finally, the programme will culminate with a unifying theory for learning to control prosthetic hands that will be tested in an NHS-approved, pre-clinical trial. Maturing this approach into a clinically-viable solution needs a dedicated team of engineers and scientists as well as a consortium of users, NHS-based clinicians and healthcare and high-tech industries. With the flexibility that a Healthcare Technologies Challenge Award affords me, I will be able to nurture and grow sustainably my multi-disciplinary team. In addition, this flexible funding will enable to focus on a converging research programme with the ultimate aim of providing prosthetic solutions that enhance NHS-approved clinical patient outcome measures significantly. Within this programme, I will identify and bring together the engineering, scientific, clinical, ethical and regulatory elements necessary to form a recognised national hub for the development of next-generation prosthetics. This work will provide the foundations for my 15-year plan to establish the Centre for Bionic Limbs. The origin of this Centre will be to act as a mechanism to safeguard engineering and scientific innovations, increase value, and accelerate transfer into commercial and clinical fields.

在世界范围内，有300多万人生活在失去上肢的情况下。最近的战争、发展中国家的工业化以及糖尿病等血管疾病导致截肢人数激增。每年增加这一人口，每2500人中就有一人出生时患有上肢缩短症。先进的假体可以在提高上肢丧失患者的生活质量方面发挥重要作用，然而，在NHS下，它们是不可用的。值得注意的是，许多创伤性肢体丧失的人在其他方面身体健康。如果他们配备了先进的假肢，并接受心理康复治疗，他们就可以独立生活，最不需要社会支持，重返工作岗位，为经济做出贡献。有太多的潜在原因限制了先进假手在临床上的广泛采用。例如，对其使用情况的调查显示，20%的上肢截肢者放弃了假肢，主要原因是这些系统的控制仍然局限于一到两个动作。此外，将假手切换到适当的握持模式，例如使用剪刀的过程是繁琐的，或者需要特别的解决方案，例如使用智能电话应用程序。其他原因包括：用户发现他们的假肢不舒服或不适合他们的需求。因此，日常工作，如系鞋带，目前对假手使用者来说是非常具有挑战性的。这些功能缺陷，再加上高昂的成本和缺乏额外益处的具体证据，已经成为限制临床采用先进假手的重大障碍。这一跨学科计划的长期目标是开发并朝着提供能够改善使用者生活质量的下一代假手的方向迈进。我们潜在的科学新颖性在于利用用户学习操作假肢的能力。例如，我们检查了肌肉的活动在多大程度上偏离了控制生物手臂和手的运动的自然模式，以及假肢使用者是否可以学习合成肌肉和假肢手指之间的这些功能地图。基于我们的试验数据，我们假设练习和获得感官反馈可以加速这种学习体验。为了解决这个根本问题，我们将采用活体实验、涉及健全志愿者的探索性研究以及肢体丧失患者的临床前工作。从这些研究中获得的洞察力将为设计新的算法提供依据，以实现对假手的无缝控制。最后，该计划将最终形成一个学习控制假手的统一理论，该理论将在NHS批准的临床前试验中进行测试。要将这种方法成熟为临床可行的解决方案，需要一支由工程师和科学家组成的专门团队，以及由用户、基于NHS的临床医生以及医疗保健和高科技行业组成的联盟。凭借医疗技术挑战奖赋予我的灵活性，我将能够培养和可持续地发展我的多学科团队。此外，这种灵活的资金将使其能够专注于一个融合的研究计划，最终目的是提供假体解决方案，显著增强NHS批准的临床患者结果衡量标准。在这项计划中，我将确定并汇集必要的工程、科学、临床、伦理和监管要素，以形成一个公认的开发下一代假肢的国家中心。这项工作将为我建立仿生肢体中心的15年计划奠定基础。该中心的初衷是作为一种机制，保护工程和科学创新，增加价值，并加快向商业和临床领域的转移。

项目成果

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

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

Abstract Decoding using Bayesian Muscle Activation Estimators.

使用贝叶斯肌肉激活估计器进行抽象解码。

• DOI: 10.1109/embc.2018.8512663
• 发表时间: 2018
• 期刊: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
• 作者: Dyson M

Data Driven Spatial Filtering Can Enhance Abstract Myoelectric Control in Amputees.

数据驱动的空间过滤可以增强截肢者的抽象肌电控制。

• DOI: 10.1109/embc.2018.8513075
• 发表时间: 2018
• 期刊: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
• 作者: Dyson M

Model-based control of individual finger movements for prosthetic hand function

基于模型的单个手指运动控制以实现假手功能

• DOI: 10.1101/629246
• 发表时间: 2019
• 作者: Blana D