Publication

@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}

}

@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{han2020_mobilehci,

title = {Context Aware Photo Protection for In-SituSharing},

author = {Zengyi Han},

year  = {2020},

date = {2020-10-05},

booktitle = {MobileHCI '20: Proceedings of the 22nd International Conference on Human-Computer Interaction with Mobile Devices and Services (Doctoral Consortium), Virtual Event, Germany},

abstract = {People nowadays are getting used to using mobile phones for daily photography, and sharing these precious moments online or in-situ with their friends. However, there is a potential risk of privacy leakage during the in-situ photo-sharing process. To address this risk, we proposeOASIS, a cOntext Aware photoprotection for in-SItuSharing behavior: using the front camera to tell different viewers, OASIScustomize viewer’s photo gallery seamlessly between different viewers according to the context of the photo. In this way, we can provide viewers with a good sharing experience while protecting the privacy of the owner. The paper is presented at MobileHCI 2020 Doctoral Consortium.},

keywords = {Context-Aware, Face Recognition, In-situ Photo Sharing, smartphone},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ono_iccetw2020,

title = {An Information Sharing Method Using P2P and V2V Communications in VANET},

author = {Shota Ono and Fuga Kikuchi and Taku Yamazaki and Takumi Miyoshi and Kaoru Sezaki},

url = {https://edas.info/showManuscript.php?m=1570630496&ext=pdf&random=1117285026&type=stamped

http://www.icce-tw.org/index.html},

year  = {2020},

date = {2020-09-28},

booktitle = {IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW 2020)},

abstract = {For realizing information sharing among vehicles, various methods based on P2P (peer-to-peer) and V2V (vehicle-to-vehicle) communications have been proposed. However, in a case of using a P2P-based method, the network load of a source vehicle will increase due to the increase of receivers. On the other hand, in a case of using a V2V-based method, vehicles cannot communicate with each other if vehicles are far apart. In this paper, we propose an information sharing method among vehicles based on V2V and P2P communications.},

keywords = {P2P, V2V, VANET},

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{10.1145/3410530.3414369,

title = {Using IOS for Inconspicuous Data Collection: A Real-World Assessment},

author = {Yuuki Nishiyama and Denzil Ferreira and Wataru Sasaki and Tadashi Okoshi and Jin Nakazawa and Anind K Dey and Kaoru Sezaki},

url = {https://doi.org/10.1145/3410530.3414369

https://github.com/tetujin/AWAREFramework-iOS},

doi = {10.1145/3410530.3414369},

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 = {261–266},

publisher = {Association for Computing Machinery},

address = {Virtual Event, Mexico},

series = {UbiComp-ISWC '20},

abstract = {Mobile Crowd Sensing (MCS) is a method for collecting multiple sensor data from distributed mobile devices for understanding social and behavioral phenomena. The method requires collecting the sensor data 24/7, ideally inconspicuously to minimize bias. Although several MCS tools for collecting the sensor data from an off-the-shelf smartphone are proposed and evaluated under controlled conditions as a benchmark, the performance in a practical sensing study condition is scarce, especially on iOS. In this paper, we assess the data collection quality of AWARE iOS, installed on off-the-shelf iOS smartphones with 9 participants for a week. Our analysis shows that more than 97% of sensor data, provided by hardware sensors (i.e., accelerometer, location, and pedometer sensor), is successfully collected in real-world conditions, unless a user explicitly quits our data collection application.},

keywords = {Effective Data Collection, iOS, Mobile Crowd sensing, Mobile Sensing Toolkit, Real-world Assessment},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-3-030-50344-4_17,

title = {iOS Crowd-Sensing Won't Hurt a Bit!: AWARE Framework and Sustainable Study Guideline for iOS Platform},

author = {Yuuki Nishiyama and Denzil Ferreira and Yusaku Eigen and Wataru Sasaki and Tadashi Okoshi and Jin Nakazawa and Anind K Dey and Kaoru Sezaki},

editor = {Norbert Streitz and Shiníchi Konomi},

url = {https://www.yuukinishiyama.com/2020/07/25/hcii2020/

https://www.yuukinishiyama.com/wp-content/uploads/2020/07/AWARE-iOS_HCII2020_preprint.pdf

https://github.com/tetujin/aware-client-ios-v2

https://github.com/tetujin/AWAREFramework-iOS},

doi = {10.1007/978-3-030-50344-4_17},

isbn = {978-3-030-50344-4},

year  = {2020},

date = {2020-07-10},

booktitle = {Distributed, Ambient and Pervasive Interactions},

pages = {223--243},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {The latest smartphones have advanced sensors that allow us to recognize human and environmental contexts. They operate primarily on Android and iOS, and can be used as sensing platforms for research in various fields owing to their ubiquity in society. Mobile sensing frameworks help to manage these sensors easily. However, Android and iOS are constructed following different policies, requiring developers and researchers to consider framework differences during research planning, application development, and data collection phases to ensure sustainable data collection. In particular, iOS imposes strict regulations on background data collection and application distribution. In this study, we design, implement, and evaluate a mobile sensing framework for iOS, namely AWARE-iOS, which is an iOS version of the AWARE Framework. Our performance evaluations and case studies measured over a duration of 288 h on four types of devices, show the risks of continuous data collection in the background and explore optimal practical sensor settings for improved data collection. Based on these results, we develop guidelines for sustainable data collection on iOS.},

keywords = {Data Collection Rate, Guideline, iOS, Mobile Sensing Framework, Sustainable Sensing},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{kamimura2020_2020Soc,

title = {スマートフォンセンサを用いた気分情報を基にした都市空間評価手法に向けて},

author = {神村潤 and 西山勇毅 and 瀬崎薫},

url = {http://www.ieice-taikai.jp/2020society/jpn/},

year  = {2020},

date = {2020-03-17},

booktitle = {電子情報通信学会 電子情報通信学会総合大会},

address = {広島大学東広島キャンパス},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Chenwei2020_LOIS,

title = {Mobile Sensing of Pedestrian Mobility and its Assessment},

author = {Chenwei Song and Masaki Ito and Yuuki Nishiyama and Kaoru Sezaki},

url = {https://www.ieice.org/ken/paper/20200312h1vo/eng/},

isbn = {0913-5685},

year  = {2020},

date = {2020-03-04},

booktitle = {IEICE Tech. Rep.},

volume = {119},

number = {477},

pages = {121--126},

publisher = {IEICE Technical Committee},

organization = {Life Intelligence and Office Information Systems (LOIS)},

abstract = {We propose a client-server system that provides crowd detection and mobility information. Our proposed system has the advantages of low cost and location flexibility without pre-deployed, as long as there is a sufficient number of users involved. We conducted several experiments in real environments to determine the feasibility, accuracy and applicable environment of the system. The result shows that the system can effectively capture the flow of people in the experimental area. In some cases, under the same environment, it can obtain almost the same mobility tracking information from fewer participating users than the GPS method.

},

keywords = {Bluetooth, Crowd detection, Human mobility},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{tadashi2019_ubi,

title = {人の限定合理性を超越した行動変容支援に向けた情報プラッ トフォームの設計},

author = {大越匡 and 西山勇毅 and 佐々木航 and 栄元優作 and 中澤仁},

url = {http://id.nii.ac.jp/1001/00200838/},

issn = {2188-8698},

year  = {2019},

date = {2019-12-03},

booktitle = {研究報告ユビキタスコンピューティングシステム（UBI）},

volume = {2019-UBI-64},

number = {8},

pages = {1--6},

publisher = { 情報処理学会},

series = {UBI64},

abstract = {ユビキタスコンピューティングシステムとして，例えば睡眠，食事，運動，学びといった人の多くの行動における行動変容を支援するための研究が数多くなされてきた一方，現状の取り組みは，人間が合理的であろうとするが種々の能力の限界によって限られた合理性しかもてない「限定合理性」(bounded rationality）を超えて人間を支援できていない．本研究では，この限定合理性を超越する情報技術の実現を目指し，その概念を整理し，解決手法における方針と情報アーキテクチャへの要件を明らかにする．},

keywords = {情報システム, 情報提示, 行動変容, 限定合理性},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{8885693,

title = {Channel Capacity Analysis of Diffusive DNA based Molecular Communication},

author = {Yao Sun and Masaki Ito and Kaoru Sezaki},

doi = {10.1109/WCNC.2019.8885693},

isbn = {978-1-5386-7646-2},

year  = {2019},

date = {2019-10-31},

booktitle = {2019 IEEE Wireless Communications and Networking Conference (WCNC)},

pages = {1-6},

publisher = {IEEE},

address = {Marrakesh, Morocco, Morocco},

abstract = {In this paper, we present a diffusion-based molecular communication using DNA molecules as data packets. In particular, we design a scheme utilizing the two types of nucleobase pairs to represent binary bits so that a bit sequence can be encoded onto a single DNA molecule. Therefore, the DNA molecule can be used as a data packet. Because of the stochastic character of the diffusion process and the variation of DNA diffusion coefficient correlated to the size of the molecule, namely number of base pairs composing the DNA fragment, the uncertainty of arrival order is a crucial factor that influences the reliable transmission. By modeling the observation window and focus slot, we concentrate on the order of the arriving DNA sequences. Based on the probability of each state, we derive the channel capacity of the proposed diffusive DNA based molecular communication. We also testify the effect of some parameters on the channel capacity, including base pair number, communication distance, and time slot width. Furthermore, the analysis shows that due to the high-density information encoded onto the DNA molecule, the proposed diffusive DNA based molecular communication has a higher channel capacity than other diffusion-based molecular communication.},

keywords = {channel capacity analysis, diffusion-based channel, DNA-based information packet, Molecular Communication},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3338286.3340133b,

title = {Challenges of Parkinson's Disease: User Experiences with STOP},

author = {Elina Kuosmanen and Valerii Kan and Julio Vega and Aku Visuri and Yuuki Nishiyama and Anind K Dey and Simon Harper and Denzil Ferreira},

url = {https://doi.org/10.1145/3338286.3340133},

doi = {10.1145/3338286.3340133},

isbn = {9781450368254},

year  = {2019},

date = {2019-10-05},

booktitle = {Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services},

publisher = {Association for Computing Machinery},

address = {Taipei, Taiwan},

series = {MobileHCI '19},

abstract = {Parkinson's disease (PD) is the second most common neurodegenerative disorder, impacting an estimated seven to ten million people worldwide. Measuring the symptoms and progress of the disease, and medication effectiveness is currently performed using subjective measures and visual estimation. We developed and evaluated a mobile application, STOP for tracking hand's motor symptoms, and a medication journal for recording medication intake. We followed 13 PD patients from two countries for a 1-month long real-world deployment. We found that PD patients are willing to use digital tools, such as STOP, to track their medication intake and symptoms, and are also willing to share such data with their caregivers and medical personnel to improve their own care.},

keywords = {empirical evaluation, logging, Parkinson's disease, smartphone},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{,

title = {Relay Node Controlled Routing Based on Location Information for Mobile-Assisted Ad Hoc Networks},

author = {Shota ono and Taku Yamazaki and Takumi miyoshi},

year  = {2019},

date = {2019-03-21},

booktitle = {2019 IEICE General Conference English Session},

address = {waseda university},

abstract = {In a mobile ad hoc network (MANET), general ad hoc routing protocols cause the traffic load due to control messages in route discovery process. To alleviate this, location-based routing protocols reduce them in the route discovery process. However, they degrade the performance due to movement of nodes since it is difficult to track the locations by only exchanging messages locally. To address the above issue, assuming the applications of MANET in urban areas, this paper proposes a relay node controlled ad-hoc routing which reduces the control messages based on location information obtained from a server via mobile networks.},

keywords = {AODV, location information, MANET, mobile network},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{yao2019,

title = {Buffer Aided Receiver for Diffusive DNA Based Molecular Communication},

author = {Yao Sun and Masaki Ito and Kaoru Sezaki},

url = {https://www.ieice.org/cs/ns/jpn/es-awards.html},

year  = {2019},

date = {2019-03-20},

publisher = {IEICE General Conference 2019},

address = {Japan},

keywords = {Diffusive DNA, Molecular Communication},

pubstate = {published},

tppubtype = {inproceedings}

}