Publication

@inproceedings{wowmon2023_han,

title = {HeadSense: Visual Search Monitoring and Distracted Behavior Detection for Bicycle Riders},

author = {Zengyi Han and Xuefu Dong and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://coe.northeastern.edu/Groups/wowmom2023/index.html},

doi = {10.1109/WoWMoM57956.2023.00043},

isbn = {979-8-3503-3165-3},

year  = {2023},

date = {2023-07-12},

urldate = {2023-07-12},

booktitle = {2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)},

pages = {281-289},

address = {Boston, Massachusetts},

abstract = {Distracted riding behavior is one of the main causes of bicycle-related traffic accidents, resulting in a large number of casualties and economic losses every year. There is an urgent need to address this problem by accurately detecting distracted riding behaviors. Inspired by the observation that distracted riding behaviors induce unique head motion features that respond to the rider's attention, we present the HeadSense, a helmet-based system that not only monitors the visual search episode of the rider but also detects distracted riding behaviors. Specifically, HeadSense leverages the inertial motion unit (IMU) to recognize distracted behaviors such as using smartphones, attracting to the roadside element, and abreast riding. We designed, implemented, and evaluated HeadSense through extensive experiments. We conducted experiments with 19 participants inside the university's campus. The experimental results show that HeadSense can achieve an overall accuracy of 86.14% while monitoring visual search episodes. Moreover, HeadSense can detect the occurrence of distracted riding behaviors with an average precision of up to 85.04%.},

keywords = {Head Movement Detection, Human Activity recognition, Mobile sensing},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}