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Details of Grant 

EPSRC Reference: EP/G014078/1
Title: Objective-based Iterative Learning Control for Robotics and Rehabilitation
Principal Investigator: Freeman, Professor CT
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Electronics and Computer Science
Organisation: University of Southampton
Scheme: First Grant Scheme
Starts: 02 March 2009 Ends: 01 April 2011 Value (£): 234,915
EPSRC Research Topic Classifications:
Biomechanics & Rehabilitation
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
16 Sep 2008 Healthcare Engineering Panel (Eng) Announced
Summary on Grant Application Form
The purpose of this project is to develop and significantly enhance an exciting new technology that has been shown to help stroke patients regain arm function. This technology is based on using Electrical Stimulation to assist subjects in performing arm movements which they cannot otherwise manage. By making muscles work, Electrical Stimulation solves the problem that people have when they try to re-learn skills after having a stroke. This problem is that re-learning skills takes practice, and this requires feedback which you can't get when you are unable to move your arm at all. The way in which people re-learn skills after a stroke is exactly the same process as you do when you learn to play tennis. You become better at it, because new nerve connections have been made within your brain and spinal cord. Not only do you need to practice, but you also need feedback of your performance so that you can correct your movement.When Electrical Stimulation is applied to muscles, electrical impulses travel along the nerves in much the same way as the electrical impulses from your brain. If stimulation is carefully controlled, a useful movement can be made. The re-learning process works better if the person is attempting the movement themselves. Recent innovative research has exploited this fact by combining a person's own effort with just enough Electrical Stimulation to achieve the intended movement. This research involved subjects performing horizontal reaching movements with Electrical Stimulation applied to their triceps. Their task was to track a spot of light as it moved slowly in front of them, and a technique called 'Iterative Learning' was used to decide what stimulation to apply to help them perform the tracking task accurately. By carefully varying this stimulation to best assist the subject, this research has established that Electrical Stimulation is able to help people re-learn movement after a stroke.The aim of this project is to maximise the therapeutic benefit of Electrical Stimulation during treatment. Foundations will also be laid that are necessary for the development of an inexpensive system that can be used in patient's own homes to increase access to this innovative technology. The way in which this will be done is to develop 'Iterative Learning' into a much more powerful and flexible tool for governing the stimulation. It will then be used to help people perform far more natural movements such as picking up a bottle and pouring from it, pressing a series of buttons, and turning a handle. Since these sort of tasks are important for day to day living, the technology then directly helps patients re-learn the tasks that are most useful to them. Another advantage is that the 'Iterative Learning' will also be able to respond to the wishes of the physiotherapist who supervises the treatment. If they decide that the movement the subject is trying to do is not ideal, they will have the freedom to change the way in which the stimulation helps the subject perform the movement. Furthermore, the simple way in which the tasks are presented to people means that very little equipment is needed, as there is no trajectory to display, no form of constraint to the movement, nor any robotic assistance used. This therefore removes a substantial obstacle preventing the technology from moving from the lab and into patient's homes.Although stroke rehabilitation is the application focussed on, the added flexibility given to 'Iterative Learning' will also benefit many processes that are found in industry. Examples of these include robots which perform the same operation over and over again in production lines. This flexibility will give the way in which the repeated task is performed the freedom to vary in order to maximise efficiency, respond to changes in the task, and satisfy desired constraints. This will all be achieved whilst still maintaining a high degree of accuracy.
Key Findings
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Potential use in non-academic contexts
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Date Materialised
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Organisation Website: http://www.soton.ac.uk