Background. The Swedish government states that participation and influence are among the most fundamental prerequisites of public health. Mobility services provide professional and high quality information, advice, and assessment to individuals with a medical condition or recovering from an accident or injury that may affect their ability to drive, access, or egress a motor vehicle. Although Swedish physicians must report drivers with medical conditions, rehabilitative training for driving licenses following injury or severe medical conditions does not exist within contemporary Swedish healthcare systems. Aim. This study aimed primarily to describe and compare two driving assessment models, i.e., the model used by Traffic Medicine Center (TRMC) Stockholm in 1998 and the model provided by Mobilitetscenter.se (MC.se) Gothenburg in 2007. The study also sought to determine the context of the changed provision of driving assessments and identify the significant components of this change. Method A literature review, synthesised with the author’s research and complemented by practice-based evidence, provided the knowledge base and starting point of this essay. Action research paired with intervention propelled the changes in the provision of mobility services in Sweden between 1998 and 2007. The author used John Ovretveit’s identification of significant change components to illuminate the complex change process that occurred.  Selection of an appropriate list of variables to compare the two assessment models derived from FORUM of Mobility Centres, the umbrella organization of British Mobility Centres that developed national guidelines for driving ability assessments in the UK; the QEF Mobility Centre was a member of that organization. The author used a questionnaire and a semi-structured interview to gather relevant data from the compared centers. Results TRMC aims primarily to provide services for society through its contributions to road safety. A Mobility Center focuses on providing assessment and rehabilitation services, optimizing impaired drivers’ mobility potential and minimizing collision risks. Cognitive assessment at TRMC accounted for a large part of this study’s time allotment. MC.se’s practical driving test in a suitably modified car provides the most significant difference between the two models.  In addition, MC.se has replaced physicians and psychologists with occupational therapists. Conclusion TRMC aims to ensure that current drivers comply with medical driving laws and regulations. MC.se provides assessments that maximize mobility potential and also ensure low collision rates. Thus, safety is balanced against the right to mobility. The study also explores coping strategies, training, adaptations, and choice of vehicle. / <p>ISBN 978-91-85721-58-0</p>

The remarkable increase in the use of sensors in our daily lives has provided an increasing number of opportunities for decision-making and automation of events. Opportunities for decision-making have further risen with the advent of smart technology and the omnipresence of sensors. Various methods have been devised to detect different events in a driving environment using smart-phones as they provide two main advantages: they remove the need to have dedicated hardware in vehicles and they are widely accessible. Rewarding safe driving has always been an important issue for insurance companies. With this intention, they are now moving toward implementing plans that consider current driving usage (Usage-based-drive plans) in contrast with traditional history-based-plans. The detection of driving events is important in insurance telematics for this purpose. Events such as acceleration and turning are good examples of important information. The sensors are capable of detecting whether a car is accelerating or braking, while through fusing services we can detect other events like speeding or the occurrence of a severe weather phenomenon that can affect driving. This thesis aims to look at the telematics from a new angle that employs smart-phones as the sensing platform. We proposed a new hybrid classification algorithm that detects acceleration-based events with an F1-score of 0.9304 and turn events with an F1-score of 0.9038. We further performed a case study on measuring the performance of driving utilizing various measures. This index can be used by a wide range of benefactors such as the insurance and transportation industries.

No description available.

Previous research has shown that cognitive deficits arising from neurological impairment can impact on driving performance. The diverse nature of cognitive, perceptual and behavioural impairments experienced by drivers with neurological impairment and the resulting impact on driving ability has been the subject of extensive research involving the use of psychometric off-road measures, road safety statistics, actual on-road driving assessments and self-report. This research has shown that some drivers can compensate for limitations in their driving skills but this is dependent upon realistic self-appraisal of driving abilities. Few studies have investigated the role of self-awareness of driving abilities on on-road driving performance in persons with neurological impairment. Aims: To investigate the relationship between self-awareness of driving related abilities in neurologically impaired drivers and on-road driving performance. Participants: Retrospective data were collated on 79 participants who were referred for Occupational Therapy driving assessment, comprising 24 with Closed Head Injury (CHI) (mean age 24.67 + 5.57 yrs), 30 with Cerebrovascular Accident (CVA) (mean age 61.00 + 9.08 yrs) and 25 with 'Other' diagnosis (mean age 50.64 + 21.14 yrs). All participants held a current driver's licence or learner's permit Results: Five predictor variables were significantly associated with the on-road driving assessment outcome including three demographic variables:- diagnosis (2(2)= 7.69, p = 0.021), time since injury/illness onset (2(2)= 6.40, p = 0.041), and mileage (2(2)= 5.84, p = 0.05); and two self-awareness variables:- reaction time (2(2)= 8.04, p = 0.018), and impulse control (2(2)= 13.47, p = 0.001). Logistic regression yielded a final best model containing two predictor variables (2(4) = 20.81, p = 0.000), including diagnosis (p = 0.02) and self-awareness of impulse control (p = 0.01). Discussion and Conclusion: Participants who over-estimated their driving abilities were more likely to fail a driving assessment or require driving rehabilitation than participants who under-estimated or accurately predicted their performance and participants with a diagnosis of CVA were more likely to fail or require driving rehabilitation than those with a CHI or 'Other' diagnosis.