Sensorimotor Learning for Control of Prosthetic Limbs

点击翻译按钮获取中文摘要

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.