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Details of Grant 

EPSRC Reference: EP/S005927/1
Title: Using artificial intelligence to share control of a powered-wheelchair between a wheelchair user and an intelligent sensor system.
Principal Investigator: Sanders, Dr DA
Other Investigators:
Gegov, Dr AE
Researcher Co-Investigators:
Project Partners:
Chailey Heritage School DRW May Key Industrial Software Systems Ltd
RCMI - Centre of excellence
Department: School of Engineering
Organisation: University of Portsmouth
Scheme: Standard Research
Starts: 01 October 2018 Ends: 30 September 2021 Value (£): 465,563
EPSRC Research Topic Classifications:
Artificial Intelligence Biomechanics & Rehabilitation
EPSRC Industrial Sector Classifications:
Information Technologies Technical Consultancy
Related Grants:
Panel History:
Panel DatePanel NameOutcome
01 Aug 2018 HT Investigator-led Panel Meeting - Aug 2018 Announced
Summary on Grant Application Form


Research will focus on the novel use of sensors and inventing new shared control systems and artificial intelligence (AI) to significantly and positively impact on the lives of both current and potential powered-wheelchair users.

Recently developed sensors will be digitised and then used in novel ways with AI to assist people with driving a powered wheelchair. This will allow some people to use a wheelchair by themselves for the first time, and will make driving and steering easier for many others. That will reduce the need for carers, improve health outcomes and give disabled people an opportunity for more independent mobility. For some it will provide mobility for the first time.

Access to independent mobility is important for self-esteem and a feeling of wellbeing. Natural independent mobility such as crawling and walking are usually acquired in the first two years of life; if this does not happen then people can find it difficult to acquire the skills later. Currently a wheelchair can provide some self-initiated mobility but it cannot be introduced unless a person has the spatial awareness, physical ability and cognitive skills to understand the concept. Being able to transport oneself has a positive effect on general development that cannot be underestimated. This research will provide that opportunity.

Research at the University of Portsmouth has already resulted in analogue collision avoidance and effort-reduction systems, so that people can drive for longer. Work at the Chailey Heritage Foundation created track systems to guide wheelchairs and novel systems that can follow a path parallel to a wall and sensors to safely detect the environment. All the devices will be redesigned as digital systems to connect them to expert systems for improved control. The new digital versions will interface to microcomputers. The new systems will interpret hand movements and tremors to improve control further. That will allow end-users to steer their powered wheelchairs without needing helpers and provide a greater sense of accomplishment and freedom, whilst simultaneously helping to reduce carer costs.

The abilities of the wheelchair user will be constantly assessed so that control gains can be automatically set for the sensor system and the human driver. This will be achieved by calculating a self-reliance factor depending on ability, tiredness, recent driving performance etc. An intelligent avoidance-factor will depend on obstacle proximity, a safety-factor will denote the ability of the driver and an assistance-factor will depend on time spent driving and tiredness. The sensor system will influence the motion of the wheelchair to compensate in those areas. This is the first time that this has been attempted.

Different AI systems will be used for different tasks to capitalise on their separate distinct strengths in diverse circumstances. An original hierarchy based upon the structure of Artificial Neural Networks will be used to integrate them. At least three AI techniques will be used to select courses of action for a wheelchair and a new Decision Making System (DMS) will be created to determine a best course of action by considering and comparing the outputs from the different artificial systems and the requirements of the human user. Each system will provide a level of confidence for a potential course of action, for example turn left, stop etc. The DMS will determine the action to take.

This EPSRC project will produce both new devices and new ways of integrating devices into wheelchairs to ensure safe navigation and personalized assistance with general low cost but automatically adjustable solutions that make the systems bespoke and adaptable in real time. This will help to ensure users achieve maximum functionality. The devices can be added to existing wheelchairs, providing a cost-effective way of improving quality of life and independence.

Key Findings
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Organisation Website: http://www.port.ac.uk