Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

11-2017

Abstract

Detecting dangerous riding behaviors is of great importance to improve bicycling safety. Existing bike safety precautionary measures rely on dedicated infrastructures that incur high installation costs. In this work, we propose BikeMate, a ubiquitous bicycling behavior monitoring system with smartphones. BikeMate invokes smartphone sensors to infer dangerous riding behaviors including lane weaving, standing pedalling and wrong-way riding. For easy adoption, BikeMate leverages transfer learning to reduce the overhead of training models for different users, and applies crowdsourcing to infer legal riding directions without prior knowledge. Experiments with 12 participants show that BikeMate achieves an overall accuracy of 86.8% for lane weaving and standing pedalling detection, and yields a detection accuracy of 90% for wrong-way riding using crowdsourced GPS traces.

Keywords

Bike, Smartphones, Activity Recognition

Discipline

Digital Communications and Networking | OS and Networks

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services: MobiQuitous 2017, Melbourne, November 7-10

First Page

313

Last Page

322

ISBN

9781450353687

Identifier

10.1145/3144457.3144462

Publisher

ACM

City or Country

Melbourne, VIC, Australia

Additional URL

https://doi.org/10.1145/3144457.3144462

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