Research Article
BikeMate: Bike Riding Behavior Monitoring with Smartphones
@INPROCEEDINGS{10.4108/eai.7-11-2017.2273590, author={Weixi Gu and Zimu Zhou and Yuxun Zhou and Han Zou and Yunxin Liu and Costas Spanos and Lin Zhang}, title={BikeMate: Bike Riding Behavior Monitoring with Smartphones}, proceedings={14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services}, publisher={ACM}, proceedings_a={MOBIQUITOUS}, year={2018}, month={4}, keywords={smartphones;personal digital assistants;}, doi={10.4108/eai.7-11-2017.2273590} }- Weixi Gu
Zimu Zhou
Yuxun Zhou
Han Zou
Yunxin Liu
Costas Spanos
Lin Zhang
Year: 2018
BikeMate: Bike Riding Behavior Monitoring with Smartphones
MOBIQUITOUS
ACM
DOI: 10.4108/eai.7-11-2017.2273590
Abstract
Detecting dangerous riding behaviors is of great importance toimprove bicycling safety. Existing bike safety precautionary mea-sures rely on dedicated infrastructures that incur high installationcosts. In this work, we propose BikeMate, a ubiquitous bicyclingbehavior monitoring system with smartphones. BikeMate invokessmartphone sensors to infer dangerous riding behaviors includ-ing lane weaving, standing pedalling and wrong-way riding. Foreasy adoption, BikeMate leverages transfer learning to reduce theoverhead of training models for different users, and applies crowd-sourcing to infer legal riding directions without prior knowledge.Experiments with 12 participants show that BikeMate achieves anoverall accuracy of 86.8% for lane weaving and standing pedallingdetection, and yields a detection accuracy of 90% for wrong-wayriding using crowdsourced GPS traces.