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{sensys2022_sukeda,

title = {Poster abstract: Recursive Queueing Estimation Using Smartphone-based Acoustic Ranging},

author = {Issey Sukeda and Hiroaki Murakami and Yuuki Nishiyama and Hiroaki Murakami and Yoshihiro Kawahara},

year  = {2022},

date = {2022-11-06},

urldate = {2022-11-06},

booktitle = {Proceedings of the 20th Conference on Embedded Networked Sensor Systems, Boston, USA},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {SenSys '22},

keywords = {Acoustic sensing, Mobile sensing, queueing},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{mobicovid22_nishiyama,

title = {MOCHA: Mobile Check-in Application for University Campuses Beyond COVID-19},

author = {Yuuki Nishiyama and Hiroaki Murakami and Ryoto Suzuki and Kazusato Oko and Issey Sukeda and Kaoru Sezaki and Yoshihiro Kawahara},

doi = {10.1145/3492866.3557736},

isbn = {978-1-4503-9165-8/22/10},

year  = {2022},

date = {2022-10-18},

urldate = {2022-10-18},

booktitle = {The Twenty-third International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing},

volume = {MobiHoc '22},

publisher = {ACM},

address = {Seoul, Republic of Korea},

abstract = {Users and operators of shared spaces must ensure safety in such areas to prevent the spread of COVID-19. Although each organization has operated a variety of safety-related systems, including contact tracing, congestion monitoring, and check-in services, it is unclear what elements, such as privacy protection level, benefits, and permission procedures, have promoted the usage of these systems. In this study, we created MOCHA, a platform for sharing and tracking room-level locations. This platform automatically detects visited places by scanning Bluetooth beacons in each room using smartphones and shares location data according to predefined user settings. The collected data is used for room-level contact tracing, congestion monitoring, and reservation services. According to >6,500 users' usage data for a year in a university, outlining the advantages of utilizing the app encouraged people to install the app, and reinforced connections in small private groups are encouraged to use the app continuously.},

keywords = {Bluetooth beacon, COVID-19, Mobile sensing, Shared space management, Social Implementation},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}

@inproceedings{10.1007/978-3-031-05431-0_23,

title = {Enhancing Self-Protection: What Influences Human’s Epidemic Prevention Behavior during the COVID-19 Pandemic},

author = {Liqiang Xu and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://doi.org/10.1007/978-3-031-05431-0_23},

doi = {10.1007/978-3-031-05431-0_23},

isbn = {978-3-031-05430-3},

year  = {2022},

date = {2022-06-01},

urldate = {2022-01-01},

booktitle = {Distributed, Ambient and Pervasive Interactions. Smart Living, Learning, Well-Being and Health, Art and Creativity: 10th International Conference, DAPI 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Virtual Event, June 26 – July 1, 2022, Proceedings, Part II},

pages = {336–351},

publisher = {Springer-Verlag},

address = {Berlin, Heidelberg},

abstract = {Under the circumstance of the rapid spread of the COVID-19 pandemic, enhancing human’s awareness of self-protection is one practical method to slow down the epidemic. In this study, we utilize mobile sensing to track human activity and guide human’s epidemic prevention behavior by gamified feedback techniques by our developed application. Virtually, human’s self-protection awareness is affected by many factors and the measures to enhance people’s self-protection behavior against the epidemic COVID-19 has always been an unresolved issue. In order to search for factors that influence human’s self-protection behavior, we analyzed the relationships between various human activities and the percentage complete of human’s self-protection behavior and we have extracted some more general conclusions from the results. Based on our data analysis results, we also made some proposals to enhance self-protection behavior. Meanwhile, our study illustrates the effectiveness of the method that analyzes human self-protection behavior through mobile sensing. Our study also validates the effectiveness of persuasive technology on human’s self-protection behavior against the COVID-19 pandemic and therefore we advocate enhancing human’s self-protection awareness through external intervention and guidance by smart device.},

keywords = {COVID-19, Infection prevention, Mobile sensing, Persuasive technology., Self-protection behavior, Self-Tracking},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{edgedl2021-uv,

title = {Towards Estimating UV Exposure Using GPS Signal Strength from a Carrying Smartphone},

author = {Soichiro Higuma and Kosuke Hatai and Yuuki Nishiyama and Kaoru Sezaki},

doi = {10.1109/SMARTCOMP52413.2021.00063},

isbn = {2693-8340},

year  = {2021},

date = {2021-08-23},

booktitle = {2021 IEEE International Conference on Smart Computing (SMARTCOMP)},

pages = {299-304},

publisher = {IEEE},

address = {Irvine, CA, USA},

abstract = {Owing to lifestyle changes, urbanization, and the COVID-19 pandemic, many people spend more time indoors and tend to receive less direct sunlight than before. Although excessive or inadequate ultraviolet (UV) exposure can be harmful to our physical and mental health, moderate UV exposure is essential for vitamin D (VD) production in the body. In this study, we estimate the UV exposure using an off-the-shelf smartphone, and explore the relationship between the UV values and GPS signal strength (C/N0). The results demonstrate that a strong correlation (R 2 = 0.73) between the UV values and carrier to noise density (C/N0) even if the smartphone and UV sensor are moved. Therefore, it is possible to estimate the UV exposure to some extent from a person's location, even while carrying a smartphone.},

keywords = {Estimation, GPS, Mobile sensing, UV},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{taxi_co2_20201,

title = {Towards Infectious Disease Risk Assessment in Taxis using Environmental Sensors},

author = {Hidenaga Ushijima and Shota Ono and Yuuki Nishiyama and Kaoru Sezaki},

editor = {Norbert Streitz and Shin'ichi Konomi},

url = {http://2021.hci.international/},

doi = {10.1007/978-3-030-77015-0_13},

isbn = {978-3-030-77015-0},

year  = {2021},

date = {2021-07-07},

booktitle = {Distributed, Ambient and Pervasive Interactions},

volume = {12782},

pages = {178--188},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {The spread of Coronavirus disease of 2019 (COVID-19) has reaffirmed the importance of ventilation in enclosed public spaces. Studies on air quality in public spaces such as classrooms, hospitals, and trains have been conducted in the past. However, the interior of a taxi, where an extremely small space is shared with an unspecified number of people, has not been sufficiently studied. This is a unique environment where ventilation is important. This study compared ventilation meth-ods focusing on the CO2 concentration in the cabin, and evaluated the frequency of ventilation in an actual taxi using sensing technology},

keywords = {CO2, COVID-19, Mobile sensing, Public Transportation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sensys2020_selfguard,

title = {SelfGuard: Semi-Automated Activity Tracking for Enhancing Self-Protection against the COVID-19 Pandemic},

author = {Yuuki Nishiyama and Takuro Yonezawa and Kaoru Sezaki},

url = {https://youtu.be/KYmvCHl_U7g},

doi = {10.1145/3384419.3430592},

isbn = {978-1-4503-7590-0/20/11},

year  = {2020},

date = {2020-11-16},

booktitle = {the 18th ACM Conference on Embedded Networked Sensor Systems (SenSys '20), COVID-19 Pandemic Response, Virtual Event, Japan },

number = {2},

pages = {780–781},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

abstract = {Contagious diseases like COVID-19 spread periodically and threaten our lives. Self-protection, such as washing hands, wearing a mask, and staying home, are simple and practical solutions to safeguard against these diseases. Most governments and health departments recommend that people maintain self-protection. Although continuous self-protection effectively prevents the spread of infection, only the intent to self-protect is unsustainable in the long term. In this study, we design, develop, and deploy an application to track users' daily activities semi-automatically and enhance self-protection behavior using mobile sensing and gamified feedback techniques. Currently, more than 324 people have installed the app via AppStore, and 52 users have shared their activity data to our research group.},

keywords = {COVID-19, GPS, Mobile sensing, Self-Tracking},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3410530.3414434,

title = {Towards Estimating UV Light Intensity Using GPS Signal Strength},

author = {Soichiro Higuma and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://doi.org/10.1145/3410530.3414434

https://youtu.be/qsuyUv47M6E

https://github.com/sezakilab/M5Stack_UVLogger},

doi = {10.1145/3410530.3414434},

isbn = {9781450380768},

year  = {2020},

date = {2020-09-12},

booktitle = {Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers},

pages = {684–687},

publisher = {Association for Computing Machinery},

address = {Virtual Event, Mexico},

series = {UbiComp-ISWC '20},

abstract = {Due to recent urbanization and changing lifestyles, many people have been spending more time indoors. Hence, they tend to receive less direct sunlight than ever before. Although excessive/inadequate UV exposure can be harmful to human health leading to illnesses such as skin cancer, spots, or depression, moderate UV exposure is necessary for vitamin D production in the body. Therefore, estimating UV exposure with a commonly used device is useful for maintaining a healthy lifestyle from excessive/inadequate UV exposure in our daily life. In this study, we aim to estimate UV exposure, and to this end, we used the GPS signal strength (C/No) collected from an off-the-shelf smartphone for exploring the relationship between UV values and C/No. We conducted an experiment and measured UV values and C/No from 10 places in two different situations. From the results, we observed a significant correlation (R2 more than 0.87) between UV values and C/No when all the data were divided by the sun/shade condition. This result supports the fact that UV values potentially can be inferred from C/No to some degree if the sun/shade condition can be detected.},

keywords = {Estimation, GPS Signal Reception, Mobile sensing, UV},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3301293.3309553,

title = {Mobility Control of Mobile Sensing for Time-Varying Parameter},

author = {Yuichi Nakamura and Masaki Ito and Kaoru Sezaki},

url = {https://doi.org/10.1145/3301293.3309553},

doi = {10.1145/3301293.3309553},

isbn = {9781450362733},

year  = {2019},

date = {2019-01-01},

booktitle = {Proceedings of the 20th International Workshop on Mobile Computing Systems and Applications},

pages = {159},

publisher = {Association for Computing Machinery},

address = {Santa Cruz, CA, USA},

series = {HotMobile ’19},

keywords = {active sensing, kriging, Mobile sensing, sensor networks},

pubstate = {published},

tppubtype = {inproceedings}

}