Sensorimotor Learning for Control of Prosthetic Limbs

Lead Research Organisation: University of Edinburgh
Department Name: Sch of Informatics


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.

Planned Impact

This programme aims at development of a new biologically-informed approach, termed Pattern Learning, for the control of upper-limb hand prostheses. The impact of this programme is as the following:

Society: Loss of the hand, and the complexities it entails, is one of the most feared conditions having a large impact upon individuals, their families and the society. In addition to the loss of function that losing a limb causes, phantom pain and psychological distress can be severe. Life-time care requires taking drugs that have side effects. Use of advanced prostheses can improve the quality of life of users dramatically and contribute to their personal dignity, independence and more effective inclusion in the society. As such, prostheses that offer seamless control of multi-joint movements are essential to minimise the effect of limb loss.

The challenge to provide effective prostheses is a recurrent feature of comments from users in Patient and Public Involvement meetings. In addition, It has been a key theme in recent national reports on prosthetic services in the UK (e.g. Chavasse report, 2014) particularly in relation to injured service personnel and survivors of major trauma. These issues are discussed regularly during meetings of the All-Party Parliamentary Limb Loss Group. The proposed programme has the potential to make a step change in the quality of control of prosthetic hands.

In addition to prosthetics, the knowledge and technologies, e.g. stimulation of the peripheral nerves, that we will develop in this programme can be translated to other movement disorders such as foot drop, a muscular weakness or paralysis that makes walking difficult because the patient cannot lift the front part of the foot and toes.

Economy: The results of this project will cement the UK's reputation as the leading country for prosthetic design and manufacturing. Three of the four major manufacturers of upper- and lower-limb prosthetic limbs are based in the UK. The market for advanced prostheses and bionics is growing rapidly. This project will provide significant know-how and novel technologies to help the UK's prosthetics industry remain competitive in a global market. Financial security of the British upper-limb prosthetics industry will help to reduce the production and R&D cost allowing the NHS to cover the cost for a larger number of patients. Moreover, able-bodied individuals who want advanced interfaces to better operate or interact with robots or games can also benefit from the results of this work. In addition, the implantable electronic technologies for neural stimulation that we develop can be taken up by the Electronic Medicine (electroceuticals) industry and used for treatment of a range of health conditions such as diabetes and depression.

Knowledge: Understanding the flexibility and limits of the human motor systems in learning new skills and their application in prosthesis control are very exciting and timely, as evident by the number recent publications in Nature on these topics. Our cross-species approach will help development of a unifying theory for Pattern Learning which is very exciting scientifically because with Pattern Learning we will be able to develop a platform for truly plug and play prosthetic systems.

People: This programme will provide me with a platform upon which I can work towards becoming a research leader in the field of prosthetics. In addition, it will provide high-quality training in the general areas of signal analysis and sensorimotor control for three research associates (RA1-3) and several PhD students. All team members will gain knowledge and expertise through the process of solving challenging problems. They will spend time at both Newcastle University's School of Electrical and Electronic Engineering and Institute of Neuroscience as well as at collaborating hospitals (Freeman and Salford Royal). They will benefit from a truly multi-disciplinary research environment.


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Dupan S (2022) How Fast Is Too Fast? Boundaries to the Perception of Electrical Stimulation of Peripheral Nerves. in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

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Garske C (2022) Increasing Voluntary Myoelectric Training Time Through Game Design. in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

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Jabban L (2022) Sensory Feedback for Upper-Limb Prostheses: Opportunities and Barriers. in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

Related Projects

Project Reference Relationship Related To Start End Award Value
EP/R004242/1 01/02/2018 30/08/2020 £1,028,683
EP/R004242/2 Transfer EP/R004242/1 31/08/2020 31/01/2024 £712,798
Description Myoelectric control schemes based on motor learning have historically provided concurrent feedback during training and assessment of participant control acuity. While impressive performance can be achieved with the assistance of such feedback mechanisms, this has little meaning unless the user has access to a similar feedback loop during real control. This is problematic when considering that, in the real-world users typically do not have access to concurrent feedback of their control input. Our results show that with appropriate training it is possible to learn and consistently reproduce distinct abstract muscle contractions in the absence of concurrent feedback. This suggests no algorithmic assistance or additional hardware is necessary to restore four grasp classes to existing dual-site control devices.
Exploitation Route This outcome justifies future prosthetics research beyond the laboratory freeing up the participants from the need to see a screen. Long term studies of motor control pave the way for next-generation prosthetic limbs.

These results provide a methodological framework for other users to move away from off-line research in prosthetics.
Sectors Healthcare

Description AI-enabled Portable Incontinence Management Device
Amount £50,399 (GBP)
Funding ID ES/W006359/1 
Organisation Economic and Social Research Council 
Sector Public
Country United Kingdom
Start 08/2021 
End 08/2022
Description Bionics+: User Centred Design and Usability of Bionic Devices
Amount £902,307 (GBP)
Funding ID EP/W00061X/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 05/2021 
End 06/2025
Description Symbiotic Intrabody Networks for Bioelectronic Therapeutics
Amount £302,148 (GBP)
Funding ID EP/W004747/1 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 08/2021 
End 09/2022
Description Porsthetics Research Beyond The lab - cocreation activity 
Form Of Engagement Activity A formal working group, expert panel or dialogue
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Patients, carers and/or patient groups
Results and Impact Two co-creation workshops were held, which included people who use upper limb prosthetic devices (hereafter called users), clinicians, academics, a policy stakeholder, and a representative from the upper-limb prosthetics industry (hereafter called professionals). The discussions during the workshops indicate that research and clinical assessment conducted remotely from a laboratory or clinic could inform future solutions that address user needs. Users were open to the idea of sharing sensor and
contextual data from within their homes to external laboratories during research studies. However, this was dependent upon several considerations, such as choice and control over data collection. Regarding clinical assessment, users had reservations of how data may be used to inform future prosthetic prescriptions whilst, clinicians were concerned with resource implications and capacity to process user data.

We reported findings of the discussions shared by participants during both workshops in a paper. The paper concludes with a conjecture that collecting sensor and contextual data from users within their home environment will contribute toward literature within the field, and potentially inform future care policies for upper limb prosthetics. The involvement of users during such studies will be critical and can be enabled via a co-creation approach. In the short-term, this may be achieved through academic
research studies, which may in the long-term inform a framework for clinical in-home trials and clinical remote assessment.
Year(s) Of Engagement Activity 2021
Description Research Showcase in Bionics 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Patients, carers and/or patient groups
Results and Impact This was an online engagement event during which UK research in bionics was showcased to people with limb difference
Year(s) Of Engagement Activity 2021