Restoration of Reach and Grasp in Stroke Patients using Electrical Stimulation and Haptic Feedback

Lead Research Organisation: University of Southampton
Department Name: Electronics and Computer Science


When you practice playing 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. In this research we are using this idea to teach people who have had a stroke how to learn new skills. When people re-learn skills after a stroke they go through the same process as you do when you learn to play tennis. But they have a problem. Because some of the nerves connecting their brain and their muscles have been damaged they can hardly move at all. Consequently they cannot practice which means they don't get feedback. Muscles can be made to work by Electrical Stimulation of the nerves leading to them. Electrical impulses travel along nerves in a similar way to electrical impulses from your brain. If stimulation is carefully controlled, a useful movement can be made. This works better if the person is attempting the movement themselves; we therefore need to combine a person's own effort with just enough extra electrical stimulation to achieve the movement. In a previous research project we designed and tested control algorithms (rules used to regulate stimulation) to stimulate one muscle during a simple reaching task. We asked patients to track a spot of light with their hand as it moved away from their body. Their forearm rested on a horizontal support that glided over a table and their hand was curved around a vertical bar. As they moved we stimulated the muscle that extended their elbow muscle. After each attempt we used the 'rules' to adjust the level and timing of the stimulation on the next to improve their tracking. After five attempts patients could track the spot of light almost perfectly. They then continued to practice; if they tracked the spot well, then the next time they got less help from the stimulation. This is called Iterative Learning and it models the way the brain learns new skills.A study with 5 stroke patients showed that it helped them to relearn to move their arm, but they didn't get much better at performing everyday tasks. To do this we believe we need to make the tasks more 'real' by being 3D and include opening the hand and grasping as well as reaching. This requires stimulating the muscles of the shoulder, elbow, wrist and fingers. Tracking a spot of light is also boring and does not give the 'touch' feedback that you experience in real-life. So, rather than tracking a spot of light, patients will play a virtual reality computer game and when they successfully grasp a virtual object they will get a sensory stimulus to the finger tips. To the make the games more fun we will adjust the level of difficulty so make it challenging and give a feeling of success and progress. To design control algorithms to adjust stimulation to multiple muscles so that they can perform real-life tasks, provide appropriate and timely sensory feedback and adjust the 'game' is the goal of this project. These are the steps to be taken to achieve our goal. Firstly, to design the rules, we will create a mathematical model of the arm and hand to predict how it will respond, taking into account things like spasticity (involuntary over-activity in some muscles) and fatigue. We then design the algorithms - this is the most challenging part of the project. To provide the sensory or 'haptic' feedback we will adapt a commercially available glove (no need to re-invent this) adding sensors, linking the information from the glove to the algorithm and making the glove easy for patients to put on and take off. Throughout the project we will talk to patients, therapists and others to make sure what we create is fit-for-purpose. Each component will be tested with healthy people and those who have had a stroke. The experimental work will culminate in an 8-week clinical trial involving between five and eight stroke patients.

Planned Impact

Society will benefit from this research - Stroke is the greatest cause of disability among older people in the UK. - Conventional upper limb rehabilitation following stroke fails to achieve effective recovery of function there is an unmet need for a large number of patients. - Pressure on services will increase as Europe's old-age dependency ratio doubles by 2050. - Cost-effective ways of improving recovery and long-term independence are vital for the UK economy and people's quality of life. - Both electrical stimulation and robotic therapy devices can address this societal need by providing cost-effective, personalised training either in hospital or, via the Internet, in peoples' own homes. - The approaches developed in this proposal significantly increase the potential of both technologies to provide effective treatment. - Society will benefit from this research through cost-effective use of healthcare resources. Patients with stroke will benefit from this research - Upper limb function is critical for independence; 80% of stroke patients experience long-term reduction in manual dexterity and half of all patients are unable to perform everyday tasks. - Although systematic reviews have provided strong evidence that training with Functional Electrical Stimulation (FES) improves motor function, currently there is little evidence that this translates into improved ability to perform activities of daily living. - This research releases the full potential of advanced FES control of the upper limb, enabling practice of complex functional rather than stereotypical movements, with precise application of FES which maximises motor relearning. - Our system has the potential for far greater improvement in quality of life and activities of daily living than existing technologies because it demonstrates intensive functional practice of integrated hand and arm movement via FES and provides realistic sensory feedback. - Conventional therapy comprises one-to-one treatment in which the therapist supports and assists the patient to perform functional activities, supplemented with exercises practiced individually at home or in a group. - Technology that provides personalised, carefully scaled and varied activities is more motivating and engaging for patients, and has the potential advantage of being able to be used independently, provide considerably longer practice sessions and many more repetitions. - Using such technology makes more effective use of the therapist's time and could be used outside the hospital - in patients' homes or in leisure centres. - The technology developed in this proposal supports this through generation of a range of data for assessment of patient's progress which are accessible both to the clinician and patient, and also through automatic task generation which supports independent training. - Access to rehabilitation technologies empowers patients to take control of their own rehabilitation, making them less dependent on healthcare professionals. Industry will benefit from this research - Advances in design, usability and subsequent generation of clinical evidence will lead to increased clinical use of both electrical stimulation systems and robot devices and thus benefit industry through increased demand. - None of the currently available commercial products using FES employ closed-loop control and this lack of precise control undermines their functionality and hence popularity. - Discussions with relevant companies have confirmed that a device for rehabilitation of reach and grasp in stroke patients is an attractive commercial opportunity and highlighted the key factors that need to, and will be, addressed to make the technology attractive to a commercial developer. - Developing the system and confirming effectiveness using a 'gold standard' and low cost system are the crucial first steps in the process.


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Freeman C T (2011) Phase-lead Iterative Learning Control Algorithms for Functional Electrical Stimulation based Stroke Rehabilitation in Proceedings of the Institution of Mechanical Engineers - Part I: Journal of Systems & Control Engineering

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Meadmore K (2013) Electrical stimulation and iterative learning control for functional recovery in the upper limb post-stroke. in IEEE ... International Conference on Rehabilitation Robotics : [proceedings]

Description Shown how to translate control algorithms developed for industrial applications, such as a robot executing a pick and place task, to robotic-assisted stroke rehabilitation where there is a pressing need to i) improve the quality and intensity of the rehabilitation and ii) enable home use. At the end of this grant a set of algorithms had been developed, experimentally verified on robotic test beds with supporting clinical trials with a small number of patients.
Exploitation Route Needs to be developed through an uncontrolled trial and the development of technology, such as wearable devices, to enable home use.
Sectors Aerospace, Defence and Marine,Education,Electronics,Healthcare,Manufacturing, including Industrial Biotechology

Description This grant funded the development of iterative learning control laws, from the engineering domain, into robotic-assisted stroke rehabilitation, with the results published in top quality outlets in both control systems and rehabilitation. This has led to a program of research, not involving the PI, into translating the progress made into technology that is suitable for home use.
First Year Of Impact 2014
Sector Healthcare,Other
Impact Types Societal