Dr. Daniel Blackburn, University of Sheffield; Dr. Vitaveska Lanfranchi, University of Sheffield; Dr. Sam Chapman, The Floow Limited
Competition Sponsor: UK Research & Innovation
Whilst population aged over 70 continues to increase, with associated increased multi-morbidities (especially neurological and cognitive) there is a need to assess safety of driving more accurately, and at scale so that those at high risk of driving accidents can be detected without unduly affecting the majority who remain safe to drive with very low risk of causing an accident. Finding new means to evaluate fitness to drive that are accurate, reflect real world driving conditions and are scalable is crucial. This is especially daunting in the context of an aging population in which the burden of co-morbidity and polypharmacy are growing.
Currently driving risk is assessed by self-reports or by medical reports, that take into account factors such as whether the person presents poor short-term memory, disorientation, lack of judgment, attention disorders, that do not accurately reflect safety to drive. For those with affected by cognitive impairments, detailed, time-consuming and expensive neuropsychological testing are undertaken in a minority and the usefulness of these in terms of predicting driving ability is unknown. Identifying new means of assessing driving behaviour and feeding back to drivers will provide support and foster self-management, which in turn will help keep more safe drivers on the road.
The project will conduct a stakeholder’s identification and user requirement phase to elicit requirements and specifications for the identification and evaluation of driving behaviour; its relationship with fitness to drive and of potential feedback mechanisms to the drivers. The focus will be first a small population of participants as representatives of healthy drivers aged over 70 and people living with MCIs (pwMCI). Data collected from participants will then be analysed and correlated to their medical diagnosis. Specific behaviour and patterns that might indicate risk will be identified.
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