@inproceedings{10.1145/3460418.3479282,
title = {HeadSense: A Head Movement Detecting System for Micro-Mobility Riders},
author = {Zengyi Han and Xuefu Dong and Yuuki Nishiyama and Kaoru Sezaki},
url = {https://doi.org/10.1145/3460418.3479282},
doi = {10.1145/3460418.3479282},
isbn = {9781450384612},
year = {2021},
date = {2021-01-01},
booktitle = {Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers},
pages = {26–27},
publisher = {Association for Computing Machinery},
address = {Virtual, USA},
series = {UbiComp '21},
abstract = {Head movement for traffic visual searching, is one of the important factors in traffic
safety. In this paper, we present the design, implementation, and preliminary evaluation
of the HeadSense, a helmet device that detects the head movement of micro-mobility
rider. HeadSense is capable of generating data streams using the embedded 9-axis inertial
measurement unit (IMU) sensor. After the process of segmentation and classification
algorithm, HeadSense can automatically detect an individual’s head movement sequence
and visual search episodes, across the rider’s entire riding journey. Experiments
with 5 participants show that our system achieves 94.7% for per-second level detection
and 80.59% F1-score for per-episode level detection.},
keywords = {Head Movement Detection, Human Activity recognition, Wearable},
pubstate = {published},
tppubtype = {inproceedings}
}