Lanfranchi, V. orcid.org/0000-0003-3148-2535, Fadlian, M. orcid.org/0000-0002-5977-1497, Koilpillai, S.G.A. et al. (4 more authors) (2023) Understanding driving behaviour in individuals with mild cognitive impairments: a naturalistic study. In: Krömker, H., (ed.) HCI in Mobility, Transport, and Automotive Systems: 5th International Conference, MobiTAS 2023, Held as Part of the 25th HCI International Conference, HCII 2023, Copenhagen, Denmark, July 23–28, 2023, Proceedings, Part II. 5th International Conference, MobiTAS 2023, Held as Part of the 25th HCI International Conference, HCII 2023, 23-28 Jul 2023, Copenhagen, Denmark. Lecture Notes in Computer Science, LNCS 14049 . Springer Nature Switzerland , pp. 263-274. ISBN 9783031359071
Abstract
Driving is a crucial function to maintain independence for many adults, however, changes in health may affect driving skills. Researchers have sought to understand how the risk from driving changes as people age or experience cognitive decline. Previous studies, whilst recognising a number of cognitive factors that correlate with driving, have not found statistical evidence that could support using any specific cognitive test to support fitness to drive assessments. In-car monitoring technology (telematics) can provide a low-cost way to monitor driving risk by understanding measurable aspects of driving which can be correlated to risk behaviours, for instance: smoothness of driving, locationally excessive speeds and aggressive acceleration behaviours.
In this paper we present preliminary results from a naturalistic study that uses telematics to collect driving behaviour data and investigate the relationship between measurable driving factors and neurological conditions under normal driving conditions.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG. |
Keywords: | Mild Cognitive Impairment; Driver Behaviour; Telematics |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Medicine and Population Health |
Funding Information: | Funder Grant number UK RESEARCH AND INNOVATION ES/V009826/1 THE ROAD SAFETY TRUST RST 231_8_231 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 01 Feb 2024 11:22 |
Last Modified: | 01 Feb 2024 11:22 |
Status: | Published |
Publisher: | Springer Nature Switzerland |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-031-35908-8_18 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:208557 |