@inproceedings{mobisys2022_han,
title = {Poster: Head Dynamics Enabled Riding Maneuver Prediction},
author = {Zengyi Han and Xuefu Dong and Yuuki Nishiyama and Kaoru Sezaki},
url = {https://www.sigmobile.org/mobisys/2022/},
year = {2022},
date = {2022-06-27},
urldate = {2022-06-27},
booktitle = {Proceedings of the 20th Annual International Conference on Mobile Systems, Applications, and Services},
publisher = {Association for Computing Machinery},
address = {Portland, Oregon},
series = {MobiSys '22},
abstract = {While micro-mobility brings convenience to the modern city, they also cause various social problems such as traffic accidents, casualties, and huge economic losses. Wearing protective equipment has become the primary recommendation for safe riding, but passive protection cannot prevent accidents from happening after all. Thus, timely predicting the rider's maneuver is essential for more active protection and buying more time to avoid potential accidents from happening. In this poster, we explore the feasibility of using riders’ head dynamics to predict their riding maneuvers. Through ten participants’ preliminary study, we observed that not only do riders’ head movements appear ahead of their maneuvers but also head movement patterns are distinct with different maneuver intentions. We then construct a deep learning network using Long Short Term Memory, achieving 89% of accuracy on maneuver prediction.},
keywords = {Activity Recognition, Mobile sensing, Wearable sensing},
pubstate = {published},
tppubtype = {inproceedings}
}