Publication

@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{sensys2022_xu,

title = {Poster abstract: Convolutional Compressed Sensing for Smartphone Acceleration Data Compression},

author = {Liqiang Xu and Yuuki Nishiyama and Masamichi Shimosaka and Kota Tsubouchi and Kaoru Sezaki},

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 = {Acceleration data, Compressed sensing, Convolutional neural network, Smartphone sensor},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sensys2022_ishioka,

title = {Poster abstract: UV index estimation leveraging GNSS sensors on smartphones},

author = {Riku Ishioka and Yuuki Nishiyama and Kota Tsubouchi and Kaoru Sezaki},

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 = {GNSS, Passive mobile sensing, smartphone, UV index estimation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sensys2022_suzuki,

title = {Poster abstract: Room Scale Localization Improvement Utilizing Stay Time Characteristics of Each Room},

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

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 = {Bluetooth low energy, iBeacon, Indoor localization, Received signal strength indicator},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3557915.3560969,

title = {A Plug-in Memory Network for Trip Purpose Classification},

author = {Suxing Lyu and Tianyang Han and Yuuki Nishiyama and Kaoru Sezaki and Takahiko Kusakabe},

url = {https://doi.org/10.1145/3557915.3560969},

doi = {10.1145/3557915.3560969},

isbn = {9781450395298},

year  = {2022},

date = {2022-11-01},

urldate = {2022-11-01},

booktitle = {Proceedings of the 30th International Conference on Advances in Geographic Information Systems},

publisher = {Association for Computing Machinery},

address = {Seattle, Washington},

series = {SIGSPATIAL '22},

abstract = {Trip purpose plays a critical role in reflecting human mobility behavior. However, it is relatively difficult to determine. With the rapid growth of urban mobility and big mobile data, utilizing these data for trip purpose classification has been a long-term objective to enhance travel demand and behavior models used in urban planning. Although studies on this topic have been extensively conducted, most past research preferred relying on traveler attributes or long-term travel histories to achieve accurate results. These data could be privacy sensitive and often do not satisfy real-world scenarios. This study addresses the problem of classifying trip purpose by only space activity information to avoid privacy conflict. 1) External memories are collected from factorized components based on the non-negative Tucker decomposition scheme. 2) These memories are extended by the cross-attention mechanism to achieve feature augmentation. 3) Subsequently, a novel concept called "latent mode alignment" is proposed. By leveraging the linear characteristics of external memories, geographic contextual latent modes are represented and matched with travel activities; this procedure is called älignment." 4) The gate mechanism controls the eventual outputs for update. The proposed plug-in memory network (PMN), combined with baseline models, effectively outperforms the original settings. Moreover, combination models are validated with strong tolerance through missing data tests, which are common and problematic in real-world scenarios. The proposed PMN is a plug-and-play design that is easy to combine with newly developed classification models, and other memory collection methods can be expected.},

keywords = {Human mobility, matrix factorization, memory network, trip purpose},

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{smc2022_dong,

title = {DoubleCheck: Single-Handed Cycling Detection with a Smartphone},

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

doi = {10.1109/SMC53654.2022.9945380},

year  = {2022},

date = {2022-10-09},

urldate = {2022-10-09},

booktitle = {2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)},

pages = {268-274},

abstract = {Riding bicycles with only one hand on the handlebar can severely undermine the operator’s steering capability and threaten road and transportation safety. Prior studies have exploited motion sensors to detect riding contexts and recognize related behaviors. Nevertheless, they fail to integrate a scheme to account for single-handed riding with elements crucial to danger prevention: awareness of the surroundings, response to danger, and convenient adoption. In this work, we proposed, designed, and implemented DoubleCheck: a smartphone-based real-time framework for cycling hand detection and distraction recognition. The method monitors handlebar holding on different road surfaces and recognizes hazardous distraction activities related to single-handed cycling using motion signals captured by a built-in inertial measurement unit in a handlebar-borne smartphone. It was designed on the premise that single-handed cycling enabled operators to adapt their body movements to different (often distracting) activities. We conducted an evaluation experiment using 22 participants on asphalt and pavement. The results indicate that DoubleCheck achieves an F1-score of 0.96 for hand detection and 0.69 for distraction recognition, demonstrating its efficacy as a candidate rider-safety precautionary measure.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ubicomp2022_shimojo,

title = {A Preliminary Study for Detecting Visual Search Behaviors During Street Walking Using Earable Device},

author = {Kazuki Shimojo and Zengyi Han and Yuuki Nishiyama and Kaoru Sezaki},

year  = {2022},

date = {2022-09-12},

urldate = {2022-09-12},

booktitle = {Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2022 ACM International Symposium on Wearable Computers},

pages = {19–20},

publisher = {Association for Computing Machinery},

address = {USA and UK},

series = {UbiComp '22},

abstract = {Map applications on smartphones are powerful navigation tools for walking among places to visit for the first time and are used widely. On the other hand, checking the application tend to cause trouble on the road such as collisions with people, cars, and objects. To prevent the troubles, we need to detect the walker’s context regarding visual search behaviors and provide appropriate navigation information for the walker. In this paper, we propose a method to detect a walker’s context regarding visual search behaviors by using motion sensors on an earable device. We collected and investigated motion and gaze data from an earable device and gaze tracker respectively during street walking from five participants. Based on the investigation, we create a machine learning model for detecting looking around, smartphone, or normal during walking 

and stopping conditions. Our evaluations show that our models can detect more than 95% of walking and stopping conditions, and 71% of three details conditions during walking, respectively},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ubicomp2022_han,

title = {A Micro-mobility Sensing System to Portray Riding Styles},

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

year  = {2022},

date = {2022-09-12},

urldate = {2022-09-12},

booktitle = {Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2022 ACM International Symposium on Wearable Computers},

pages = {19–20},

publisher = {Association for Computing Machinery},

address = {USA and UK},

series = {UbiComp '22},

abstract = {Riding style concerns the way a micro-mobility rider chooses to ride, and it plays a significant role in traffic safety. Portraying a rider’s 

riding style is a useful way to guide them towards safer riding behaviors, and offer fine-grained information for insurance companies 

and bike-sharing companies to provide better services. To this end, we propose a micro-mobility sensing system to portray riding 

styles. Utilizing helmet-mounted and bicycle handle-mounted inertial sensors, our sensing system is able to monitor the micro-mobility 

movement status, detect the maneuver behaviors, and the safety check condition of riders. With 10 participants’ experimental data, 

we present the feasibility of detecting maneuver behavior and recording the data that characterize the rider’s riding style with our 

sensing system, and therefore make it a viable addition to the micro-mobility.},

keywords = {},

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{smartcomp2022_komatsu,

title = {Toward Measuring Conversation Duration Using a Wristwatch-type Wearable Device},

author = {Yuki Komatsu and Kazuki Shimojo and Yuuki Nishiyama and Kaoru Sezaki},

year  = {2022},

date = {2022-06-24},

urldate = {2022-06-24},

booktitle = {2022 IEEE International Conference on Smart Computing (SMARTCOMP)},

publisher = {IEEE Computer Society},

address = {Espoo, Finland},

keywords = {Context-recognition, Conversation Events, Edge Processing, Sound Classification, Wearable Computing},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{smartcomp2022_kasahara,

title = {Detecting Childcare Activities Using an Off-the-shelf Smartwatch},

author = {Yuki Kasahara and Yuuki Nishiyama and Kaoru Sezaki},

year  = {2022},

date = {2022-06-01},

urldate = {2022-06-01},

booktitle = {2022 IEEE International Conference on Smart Computing (SMARTCOMP)},

publisher = {IEEE Computer Society},

address = {Espoo, Finland},

keywords = {Activity Recognition, Childcare Activity, 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{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},

doi = {10.1109/PerComWorkshops53856.2022.9767429},

isbn = {978-1-6654-1648-1},

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{9982766,

title = {Detecting Face-Mask Wearing Status Using Motion Sensors in Commercially Available Smartwatches},

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

doi = {10.1109/HealthCom54947.2022.9982766},

year  = {2022},

date = {2022-01-01},

booktitle = {2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom)},

pages = {107-112},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{igessc2021_peng,

title = {Estimation of Greenhouse Gas Emission Reduction from Shared Micromobility System},

author = {Helinyi Peng and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://ieeexplore.ieee.org/document/9618701

https://www.youtube.com/watch?v=vbt622kXNuU},

doi = {10.1109/IGESSC53124.2021.9618701},

issn = {2640-0138},

year  = {2021},

date = {2021-11-01},

urldate = {2021-11-01},

booktitle = {2021 IEEE Green Energy and Smart Systems Conference (IGESSC)},

pages = {1-6},

publisher = {IEEE},

address = {Long Beach, CA, USA},

abstract = {Shared micromobility is widely recognized as an environmentally friendly travel mode and a critical component of transportation decarbonization. However, quantitatively assessing its environmental impact using real-world trip data is an unresolved and challenging subject. In this research, we proposed a system combining machine learning algorithms and the Monte Carlo simulation to address this issue. First, several machine learning algorithms (Random Forest, XGBoost, and LightGBM) were utilized to identify citizens’ travel mode choice preferences and then estimate the substituted travel mode of each micromobility trip. Second, to ensure the reliability of the final environmental impact assessment, the Monte Carlo simulations were used to simulate the substituted mode of each trip. Then the environmental impacts were calculated based on the life cycle greenhouse gas emissions. Instead of estimating a specific number, we obtained a probabilistic outcome for environmental impacts by using the Monte Carlo simulation, which considers the uncertainty. According to the studies, the shared bike service and the shared e-scooter service have positive environmental impacts. However, these effects are limited compared to the transportation sector’s total emissions. The most compelling reason is that shared micromobility comprises a minuscule part of total urban travel.},

keywords = {environmental impacts, greenhouse gas emission, Machine learning, shared micromobility},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{duc_vtc2021,

title = {A Run-time Dynamic Computation Offloading Strategy in Vehicular Edge Computing},

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

url = {https://events.vtsociety.org/vtc2021-fall/

https://ieeexplore.ieee.org/document/9625245},

doi = {10.1109/VTC2021-Fall52928.2021.9625245},

year  = {2021},

date = {2021-09-27},

urldate = {2021-09-27},

booktitle = {2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)},

publisher = {IEEE},

address = {Online},

abstract = {In vehicular edge computing (VEC), offloading the tasks to the nearby resource-rich edge servers helps each vehicle enhance computational capabilities and improve in-vehicle applications' performance. However, the concentration of travel at specific spaces and times poses significant challenges on the load-balancing and scheduling of computation tasks at the edge servers. This paper studies a low-complexity dynamic online offloading strategy that efficiently reduces task delay and computing resource consumption in the multi-user, multiserver vehicular edge computing scenarios. Our design addresses issues of computation task placement and execution order of the tasks on each server. We use a realistic approach that vehicles generate tasks over time, and the set of the tasks is unknown in advance so that the offloading decisions are made in runtime. Extensive simulations are conducted on a real mobility trace of Luxembourg city, and the results show that the proposed algorithm effectively improves the offloading utility of the system.},

keywords = {Connected vehicles, Edge computing, Off-loading, Vehicular networks},

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},

booktitle = {2021 IEEE International Intelligent Transportation Systems Conference (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 = {e-scooter, Micro-Mobility, Mobile Sensing Toolkit},

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},

year  = {2021},

date = {2021-09-19},

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

journal = {2021 IEEE International Conference on Intelligent Transportation - ITSC},

publisher = {IEEE},

keywords = {data analysis, e-scooter},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ieice2021_duc,

title = {An Online Task Offloading Strategy in Vehicular Edge Computing},

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

url = {http://www.ieice-taikai.jp/2021society/jpn/},

year  = {2021},

date = {2021-09-14},

urldate = {2021-09-14},

booktitle = {IEICE Society Conference 2021 },

publisher = {IEICE},

keywords = {Connected vehicles, Edge computing, Off-loading, Vehicular networks},

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{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}

}

@inproceedings{10.1145/3460418.3479283,

title = {Experience Sampling Tool for Repetitive Skills Training in Sports Using Voice User Interface},

author = {Yuuki Nishiyama and Kaoru Sezaki},

url = {https://doi.org/10.1145/3460418.3479283},

doi = {10.1145/3460418.3479283},

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 = {54–55},

publisher = {Association for Computing Machinery},

address = {Virtual, USA},

series = {UbiComp '21},

abstract = {Repetitive skills training (RST) is a commonly used method for improving skills. 

Although wearable devices and existing context-aware technologies allow us to easily 

detect objective data during RST, subjective data have not been collected effectively, 

even though both objective and subjective data are important for RST. In this paper, 

we propose and implement a prototype system, called MiQ, to collect subjective data 

with minimum workload during RST for sports. MiQ allows us to record subjective data 

hands-free via a voice user interface (VUI). We also discuss the future scope of the 

proposed prototype system.},

keywords = {context-awareness, Experience Sampling, Voice User Interface, Wearable Devices},

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}

}