@inproceedings{baig-etal-2022-drivingbeacon,
title = "{D}riving{B}eacon: Driving Behaviour Change Support System Considering Mobile Use and Geo-information",
author = "Baig, Jawwad and
Chen, Guanyi and
Lin, Chenghua and
Reiter, Ehud",
editor = "Krahmer, Emiel and
McCoy, Kathy and
Reiter, Ehud",
booktitle = "Proceedings of the First Workshop on Natural Language Generation in Healthcare",
month = jul,
year = "2022",
address = "Waterville, Maine, USA and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.nlg4health-1.1/",
pages = "1--8",
abstract = "Natural Language Generation has been proved to be effective and efficient in constructing health behaviour change support systems. We are working on DrivingBeacon, a behaviour change support system that uses telematics data from mobile phone sensors to generate weekly data-to-text feedback reports to vehicle drivers. The system makes use of a wealth of information such as mobile phone use while driving, geo-information, speeding, rush hour driving to generate the feedback. We present results from a real-world evaluation where 8 drivers in UK used DrivingBeacon for 4 weeks. Results are promising but not conclusive."
}
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%0 Conference Proceedings
%T DrivingBeacon: Driving Behaviour Change Support System Considering Mobile Use and Geo-information
%A Baig, Jawwad
%A Chen, Guanyi
%A Lin, Chenghua
%A Reiter, Ehud
%Y Krahmer, Emiel
%Y McCoy, Kathy
%Y Reiter, Ehud
%S Proceedings of the First Workshop on Natural Language Generation in Healthcare
%D 2022
%8 July
%I Association for Computational Linguistics
%C Waterville, Maine, USA and virtual meeting
%F baig-etal-2022-drivingbeacon
%X Natural Language Generation has been proved to be effective and efficient in constructing health behaviour change support systems. We are working on DrivingBeacon, a behaviour change support system that uses telematics data from mobile phone sensors to generate weekly data-to-text feedback reports to vehicle drivers. The system makes use of a wealth of information such as mobile phone use while driving, geo-information, speeding, rush hour driving to generate the feedback. We present results from a real-world evaluation where 8 drivers in UK used DrivingBeacon for 4 weeks. Results are promising but not conclusive.
%U https://aclanthology.org/2022.nlg4health-1.1/
%P 1-8
Markdown (Informal)
[DrivingBeacon: Driving Behaviour Change Support System Considering Mobile Use and Geo-information](https://aclanthology.org/2022.nlg4health-1.1/) (Baig et al., NLG4Health 2022)
ACL