Using artificial intelligence to share control of a powered-wheelchair between a wheelchair user and an intelligent sensor system.

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.

Research will benefit people with Multiple-sclerosis, Arthritis, Stroke, Paraplegia, Orthopedic-impairment, Cerebral-palsy and Diabetes, especially if blind or with missing or damaged-limb(s). Initially, disabled community groups, carers, users and families who have used the analogue systems are expected to adopt the new systems. They and their families are keen to hear about the research and are already engaging; they are keen to give their opinions to help guide the research.

Health
Work will directly benefit disabled and older users and their quality of life will be significantly enhanced. New systems will allow people with limited dexterity to use wheelchairs; users will drive for longer and more safely. The digital and AI systems will lead to new, faster and responsive processes that will replace some of the older systems in schools and institutions, hence further improving lives. It will make a significant positive difference by giving disabled individuals more confidence, independence and freedom, especially people with limited spatial awareness or cognitive ability. The techniques could also contribute to the digital hospital component of the Research Council's UK Digital economy programme involving real-time data fusion and patient tracking, and to the Healthcare Technologies theme.

Professional services
Professional UK guidelines and training were informed by previous analogue research, and new technical standards and clinical protocols were introduced. The new systems will do the same, prompting changes in professional practices in that powered-wheelchairs will be considered as an option, even for blind children, and new technical standards and clinical protocols will need to be introduced. Health outcomes will improve because of the new systems and the work will lead to new methodologies for therapists to teach people how to drive.

Cost Reduction
The research will introduce some autonomy and reduce the need for carers. Further, the costs of the systems should be significantly reduced due to digitization. Health outcomes will be further improved because of the availability of the new systems and the new ways that people will be trained on them and improvements to mental health and wellbeing will reduce NHS costs.

Schools, NHS, and institutions
Beneficiaries will gain directly through links to CHF and other institutions; time between discovery, research and use will be short as new theoretical knowledge and systems will be passed to CHF for testing, use and immediate impact. Systems will be quickly proved in schools where they will have an immediate beneficial effect. With the help of CHF, they will quickly move on to be used by the NHS, disabled community groups, in private homes and by individuals. This project also provides predictive tools to determine system use over time. That will support clinicians to assess intervention needs. Developing such techniques and technology is of key interest to powered wheelchair specialists in the medical and healthcare industries.

UK Industry
Wheelchair manufacturers will have access to new systems that can easily be added to their existing wheelchairs and software companies will have access to the new software. The UK is short of rehabilitation engineers and this project will help address that by producing two more postdocs working in rehabilitation technology. Car manufacturers will have access to the new technology for driverless vehicle research. The work will be of immediate use to Dynamics Controls and Penny & Giles and of interest to the defence and automotive industries (tracking, driving, navigation) and any innovation in this field is quickly adopted. The research could result in a new start-up to produce some of the new systems. Research in sensor fusion and AI will feed forward (and across) to Delphi UK who are developing automotive software and sensors, and Jaguar Land Rover who want to deploy self-driving cars on British roads.