Project overview
Stroke is the leading cause of adult disability in the UK. Every year 150,000 strokes occur and 54,000 of these fail to regain upper limb function, resulting in yearly personal and societal care costs of £5.5bn. These numbers will increase with the aging population; by 2040 the number of people over 65 is expected to grow by 67%. The government has termed this situation 'a ticking time bomb' and has called for innovative technology that Persons with Stroke (PwS) can use in their own homes. Functional Electrical Stimulation (FES) of muscles is a technology that has been shown to help PwS re-learn lost skills by enabling them to practice and regain lost arm movement, and in-so-doing create new nerve connections in their brain. FES works by stimulating muscles with electrical pulses via electrodes placed on the skin. Unfortunately, commercial FES systems are not suitable for intensive daily use as they are rigid and uncomfortable, and not able to assist PwS in performing the necessary precise movements because only a limited number of muscles are stimulated. In our previous research we have developed a prototype FES array on a conventional wearable fabric enabling the FES to be worn as normal clothing to achieve rehabilitation. The FES array thus is flexible, breathable and comfortable to wear, and can be scaled up to cover as many muscles as are needed. A range of precise hand functions including pinching, pointing and hand opening have been achieved by stimulating an optimised selection of electrode elements in the array. The stimulation is controlled using advanced software called iterative learning control which mimics the way the brain learns new skills. This controller can potentially achieve highly accurate movement by learning the optimal stimulation pattern over multiple attempts at a task. Our project will use printing to fabricate customised FES garments to precisely fit the individual's arm and specific needs. The customised FES array design will be generated by scanning the arm using a commercial 3D scanner and processing the image using software developed in this project. Each FES array will be printed on standard everyday fabric and then integrated into a piece of clothing (e.g. cuff/armband, sleeve, long sleeved T-shirt). The resulting garment will be very comfortable to wear and convenient to use every day. The FES clothing will be operated using a wireless control system combined with sensors which automatically adjust the FES to enable precise activities, such as assisting eating, washing and dressing. We will work closely with an expert user group consisting of PwS and their carers, FES engineers and healthcare professionals to produce a detailed device specification. This will provide the device requirements in terms of comfort, robustness, stimulation function and cost criteria. Following development, the device will be tested against this specification and refined throughout the project to ensure it fully meets the needs of PwS. Our technology will bring affordable, effective physical therapy into the homes of PwS, allowing them to practice goal-orientated functional activities at home without needing a carer or therapist. It thereby increases the intensity of rehabilitation without an increase in clinical contact time. This will lead to better outcomes, such as reduced impairment, greater restoration of function, improved quality of life and increased social activity. This in turn will translate to greater independence leading to less dependence on carers, and the possibility of return to work. The first application of the technology will be with PwS with upper limb impairments followed by those with lower limb impairments. The technology can also potentially be further applied to treat other neurological conditions such as spinal cord injuries and multiple sclerosis.
Staff
Lead researchers
Other researchers
Collaborating research institutes, centres and groups
Research outputs
Tyler Ward, Neil Grabham, Christopher Freeman, Yang Wei, Ann-Marie Hughes, Conor Power, Michael Tudor & Kai Yang,
2020, Electronics, 9(7), 1-13
Type: article
Meijing Liu, Tyler Ward, Dan Young, Helga Matos, Yang Wei, Joanna Adams & Kai Yang,
2020, Sensors and Actuators A: Physical, 303
Type: article
Wei Yang, Kai Yang, Martin Browne, Luciana Bostan & Peter Worsley,
2019, IEEE Sensor Letters, 3(2)
Type: article
Katie Meadmore, Emma Hallewell, Christopher Freeman & Ann-Marie Hughes,
2018, Topics in Stroke Rehabilitation
Type: article
Kai Yang, Katie Meadmore, Chris Freeman, Neil Grabham, Ann-Marie Hughes, Yang Wei, Russel Torah, Monika Glanc-Gostkiewicz, Steve Beeby & John Tudor,
2018, Sensors, 18(8)
DOI: 10.3390/s18082410
Type: article
Christopher Freeman, Matthew W. Spraggs, Ann-Marie Hughes, Kai Yang, Michael Tudor & Neil Grabham,
2018
Type: conference