Sezaki & Nishiyama Laboratory
Institute of Industrial Science / Center for Spatial Information Science in The University of Tokyo
Institute of Industrial Science / Center for Spatial Information Science in The University of Tokyo
Zengyi Han, Xuefu Dong, Yuuki Nishiyama, Kaoru Sezaki
HeadSense: Visual Search Monitoring and Distracted Behavior Detection for Bicycle Riders Inproceedings
In: 2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 281-289, Boston, Massachusetts, 2023, ISBN: 979-8-3503-3165-3.
Abstract | BibTeX | タグ: Head Movement Detection, Human Activity recognition, Mobile sensing | Links:
@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}
}
Xuefu Dong, Zengyi Han, Yuuki Nishiyama, Kaoru Sezaki
Detecting Single-Hand Riding with Integrated Accelerometer and Gyroscope of Smartphone Inproceedings
In: 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, pp. 19–20, Association for Computing Machinery, Virtual, USA, 2021, ISBN: 9781450384612.
Abstract | BibTeX | タグ: Accelerometer, Gyroscope, Human Activity recognition | Links:
@inproceedings{10.1145/3460418.3479294,
title = {Detecting Single-Hand Riding with Integrated Accelerometer and Gyroscope of Smartphone},
author = {Xuefu Dong and Zengyi Han and Yuuki Nishiyama and Kaoru Sezaki},
url = {https://doi.org/10.1145/3460418.3479294},
doi = {10.1145/3460418.3479294},
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 = {19–20},
publisher = {Association for Computing Machinery},
address = {Virtual, USA},
series = {UbiComp '21},
abstract = {Single-hand cycling poses a safety threat with the decrement of riders’ response
capacity. Recognizing risky behavior by prevalently used smartphones could lead to
enhanced riding safety. In this work, we propose a single-hand cycling recognition
method based on motion data acquired from the three-axis accelerometer and gyroscope
integrated into a handlebar-installed smartphone. We conducted a 4-person experiment.
The data result demonstrates that motion data of double-hand cycling clearly distinguishes
from that of single-hand, revealing the chance to materialize a robust detection tool
in smartphones to enable safer biking. For future work, we prepare to redesign the
experiment under more sophisticated circumstances with an improved platform, thus
scaling this sensing method for real-life usage.},
keywords = {Accelerometer, Gyroscope, Human Activity recognition},
pubstate = {published},
tppubtype = {inproceedings}
}
Zengyi Han, Xuefu Dong, Yuuki Nishiyama, Kaoru Sezaki
HeadSense: A Head Movement Detecting System for Micro-Mobility Riders Inproceedings
In: 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, pp. 26–27, Association for Computing Machinery, Virtual, USA, 2021, ISBN: 9781450384612.
Abstract | BibTeX | タグ: Head Movement Detection, Human Activity recognition, Wearable | Links:
@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}
}