Abstract
Elderly drivers will be more present on the road in the next few years. Mobility is fundamental for the elderly because it allows them to maintain an active lifestyle. But the elderly may suffer from cognitive, physical or sensorial decline due to aging. To help them to drive, context-aware systems can assess the status of a driver and warn him or her about hazards. We present a data analysis tool for car driving context information that includes data mining and statistical evaluation algorithms. We applied our system to data collected by sensors into an instrumented vehicle in realistic driving conditions. Results show that our tool is able to store the contextual information collected and to enable an interactive visualization of the data collected. Thanks to this tool, it is easier to share information among the scientists working on the data. Moreover, it makes it convenient to store data in the cloud.
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Acknowledgments
We want to particularly thank the Canadian Automobile Association (CAA) Foundation, Section of the Quebec Province, for funding the research works behind this paper.
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Ruer, P., Gouin-Vallerand, C., Zhang, L., Lemire, D., Vallières, E.F. (2015). An Analysis Tool for the Contextual Information from Field Experiments on Driving Fatigue. In: Christiansen, H., Stojanovic, I., Papadopoulos, G. (eds) Modeling and Using Context. CONTEXT 2015. Lecture Notes in Computer Science(), vol 9405. Springer, Cham. https://doi.org/10.1007/978-3-319-25591-0_13
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DOI: https://doi.org/10.1007/978-3-319-25591-0_13
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