Publication

@inproceedings{percom2022_dong,

title = {DoubleCheck: Detecting Single-Hand Cycling with Inertial Measurement Unit of Smartphone},

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

url = {https://youtu.be/AW5B-cPdeFI

https://www.percom.org/},

doi = {10.1109/PerComWorkshops53856.2022.9767429},

isbn = {978-1-6654-1647-4},

year  = {2022},

date = {2022-03-21},

urldate = {2022-03-21},

booktitle = {IEEE International Conference on Pervasive Computing and Communications (PerCom)},

publisher = {IEEE},

address = {Pisa, Italy},

abstract = {Riding bikes with only one hand on the handlebar can severely undermine the steering capability of riders and risk road safety. In this study, we propose a first detection framework for monitoring single-hand cycling on bicycle travel, called DoubleCheck. It is based on the premise that riders adapt their body movement during single-hand cycling, which is distinguishable to the sensors even amid noise from the exasperate road surface. The system can detect handlebar-holding under different road conditions using motion signals from a built-in inertial measurement unit (IMU) in a handlebar-mounted smartphone. We implemented the system and invited 10 participants for our evaluation experiment. Our results show that DoubleCheck achieved an F1-score of 0.94 for hand detection, proving its efficacy for real-life implementation to improve road safety.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{itsc2021_ntf,

title = {An Unsupervised Learning-based Approach for User Mobility Analysis of E-Scooter Sharing Systems},

author = {Hidenaga Ushijima and Shunsuke Aoki and Peng Helinyi and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://2021.ieee-itsc.org/},

doi = {10.1109/ITSC48978.2021.9564616},

isbn = {978-1-7281-9141-6},

year  = {2021},

date = {2021-10-25},

urldate = {2021-10-25},

booktitle = {2021 IEEE International Intelligent Transportation Systems Conference (ITSC)},

pages = {1425-1430},

publisher = {IEEE},

abstract = {Human mobility analysis is a key method for understanding urban dynamics and mobility optimization. Novel last-mile mobility, called micromo-bilities, that includes shared bicycles, electric bicycles (e-bikes), and electric scooters are seeing rapid widespread acceptance in major cities. Compared with existing mobility data such as cars, buses, and trains, the majority trip distance of micromobilities is short, typically less than a few miles. The riders use them for commuting, sightseeing, shopping, and/or fun. By using the mobility data of micromobilities, we can observe more fine-grained human mobility in urban areas than existing data sources. In this paper, we present an unsupervised learning-based technique to analyze human mobility in urban areas and to study user clusters for such micromobility services. In our approach, we cluster user mobility patterns by using non-negative tensor factorization (NTF) from area-based trip data (which only included locations of origin and destination). Our approach was applied to micromobility data collected from Chicago and Washington, D.C., and we observed characteristic patterns.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ieee_itsc_mimosense,

title = {MiMoSense: An Open Crowdsensing Platform for Micro-Mobility},

author = {Zengyi Han and Hong Duc Nguyen and Shunsuke Aoki and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://2021.ieee-itsc.org/},

doi = {10.1109/ITSC48978.2021.9564524},

year  = {2021},

date = {2021-09-19},

urldate = {2021-09-19},

booktitle = {2021 IEEE International Conference on Intelligent Transportation (ITSC)},

pages = {1-6},

publisher = {IEEE},

abstract = {The use of micro-mobility (e.g., bicycle and scooter) and their data for urban sensing and rider assessment is becoming increasingly popular in research. However, different research topics require different sensor setups; no general data collecting tools for the micro-mobility makes the researcher who wishes to collect data has to build their own collecting system from scratch. To this end, we present MiMoSense, an open crowdsensing platform for micro-mobility. MiMoSense consists of two components: (1) MiMoSense server, which is set up on the cloud, and used to manage sensing studies and the collected data for research and sharing. (2) MiMoSense client, uses micro-mobility carrying various sensors and IoT devices to collect multiple kinds of data during traveling. As a reusable open-source software, MiMoSense shifts the researcher's focus from software development to sensing data analysis; it can help researchers quickly develop an extensible platform for collecting micro-mobility's raw sensing data and inferring traveling context. We have evaluated MiMoSense's battery consumption, message latency and discuss its use.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}