Faculty Profiles - YONEZAWA Takuro

Papers - YONEZAWA Takuro

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XR Communication System for Remote Control Wheelchairs. Reviewed

Hironori Shimosato, Nozomi Hayashida, Kenta Urano, Takuro Yonezawa, Nobuo Kawaguchi

Proceedings of the 12th International Conference on the Internet of Things(IOT) page： 219 - 223 2022

Synthetic People Flow: Privacy-Preserving Mobility Modeling from Large-Scale Location Data in Urban Areas. Reviewed

Naoki Tamura, Kenta Urano, Shunsuke Aoki 0001, Takuro Yonezawa, Nobuo Kawaguchi

MobiQuitous Vol. 419 page： 553 - 567 2022

Smartphone Localization with Solar-Powered BLE Beacons in Warehouse. Reviewed

Kazuma Kano, Takuto Yoshida, Nozomi Hayashida, Yusuke Asai, Hitoshi Matsuyama, Shin Katayama, Kenta Urano, Takuro Yonezawa, Nobuo Kawaguchi

Distributed, Ambient and Pervasive Interactions. Smart Environments, Ecosystems, and Cities - 10th International Conference Vol. 13325 page： 291 - 310 2022

Deep Learning for Ballroom Dance Recognition: A Temporal and Trajectory-Aware Classification Model With Three-Dimensional Pose Estimation and Wearable Sensing Reviewed

Hitoshi Matsuyama, Shunsuke Aoki, Takuro Yonezawa, Kei Hiroi, Katsuhiko Kaji, Nobuo Kawaguchi

IEEE Sensors Journal Vol. 21 ( 22 ) page： 25437 - 25448 2021.11

An End-to-End BLE Indoor Localization Method Using LSTM

Urano Kenta, Hiroi Kei, Yonezawa Takuro, Kawaguchi Nobuo

Journal of Information Processing Vol. 29 ( 0 ) page： 58 - 69 2021

Language：English Publishing type：Research paper (scientific journal) Publisher：Information Processing Society of Japan

<p>This paper proposes an indoor localization method for Bluetooth Low Energy (BLE) devices using an end-to-end LSTM neural network. We focus on a large-scale indoor space where there is a tough environment for wireless indoor localization due to signal instability. Our proposed method adopts end-to-end localization, which means input is a time-series of signal strength and output is the estimated location at the latest time in the input. The neural network in our proposed method consists of fully-connected and LSTM layers. We use a custom-made loss function with 3 error components: MSE, the direction of travel, and the leap of the estimated location. Considering the difficulty of data collection in a short preparation term, the data generated by a simple signal simulation is used in the training phase, before training with a small amount of real data. As a result, the estimation accuracy achieves an average of 1.92m, using the data collected in GEXPO exhibition in Miraikan, Tokyo. This paper also evaluates the estimation accuracy assuming the troubles in a real operation.</p>

DOI： 10.2197/ipsjjip.29.58

Scopus

CiNii Research

A Basic Study on Ballroom Dance Figure Classification with LSTM Using Multi-modal Sensor Reviewed

Matsuyama H., Hiroi K., Kaji K., Yonezawa T., Kawaguchi N.

Smart Innovation, Systems and Technologies Vol. 204 page： 209 - 226 2021

Language：Japanese Publishing type：Research paper (scientific journal) Publisher：Smart Innovation, Systems and Technologies

The paper presents a ballroom dance figure classification method with LSTM using video and wearable sensors. Ballroom dance is a popular sport among people regardless of age or sex. However, learning ballroom dance is very difficult for less experienced dancers as it has many complex types of “dance figures”, which is a completed set of footsteps. Therefore, we aim to develop a system to assist dance exercise which gives advice proper to each dance figure characteristic by recognizing dance figures correctly. While the major approach to recognize dance performance is to utilize video, we cannot simply adopt it for ballroom dance because the images of dancers overlap each other. To solve the problem, we propose a hybrid figure recognition method combining video and wearable sensors to enhance its accuracy and robustness. We collect video and wearable sensor data of seven dancers including acceleration, angular velocity, and body parts location change by pose estimation. After that, we preprocess them and put them into an LSTM-based deep learning network. As a result, we confirmed that our approach achieved an F1-score of 0.86 for 13 figure types recognition using the multi-modal sensors with trial-based fivefold cross-validation. We also performed user-based cross-validation, and sliding window algorithms. In addition, we compared the results with our previous method using Random Forest and also evaluated the robustness with occlusions. We found the LSTM-based method worked better than Random Forest with keypoint data. On the other hand, LSTM could not perform well with a sliding window algorithm. We consider the LSTM-based method would work better with a larger dance figure data, which is our next work. In addition, we will investigate how to solve occlusion problems with pose estimation.

DOI： 10.1007/978-981-15-8944-7_13

Scopus

A Gaze-Based Unobstructive Information Selection by Context-Aware Moving UI in Mixed Reality. Reviewed

Nozomi Hayashida, Hitoshi Matsuyama, Shunsuke Aoki, Takuro Yonezawa, Nobuo Kawaguchi

Distributed, Ambient and Pervasive Interactions - 9th International Conference Vol. 12782 LNCS page： 301 - 315 2021

Attention-Guidance Method Based on Conforming Behavior of Multiple Virtual Agents for Pedestrians Reviewed

Yoshida, N; Yonezawa, T

PROCEEDINGS OF THE 21ST ACM INTERNATIONAL CONFERENCE ON INTELLIGENT VIRTUAL AGENTS (IVA) page： 209 - 215 2021

Language：Japanese Publishing type：Research paper (scientific journal) Publisher：Proceedings of the 21st ACM International Conference on Intelligent Virtual Agents, IVA 2021

Humans observe the behavior of others and exhibit conforming behavior according to the situation. In this study, we propose a method to change the user's walking behavior and attention by changing the ratio of multiple virtual agents in action. We verified whether the attention to a poster on the wall and the walking behavior of a pedestrian changed according to the type and proportion of actions exhibited by multiple agents presented to a particular pedestrian. The results showed that the participant's walking speed and attention to the wall advertisement changed according to the agents' walking behavior, such as deceleration and stopping.

DOI： 10.1145/3472306.3478346

Web of Science

Scopus

DigiMobot: Digital Twin for Human-Robot Collaboration in Indoor Environments. Reviewed

Yuto Fukushima, Yusuke Asai, Shunsuke Aoki, Takuro Yonezawa, Nobuo Kawaguchi

IEEE Intelligent Vehicles Symposium(IV) Vol. 2021-July page： 55 - 62 2021

RGNet: Robust Gravity Estimation Neural Network for IMU-based Localization Using Smartphone. Reviewed

Takuto Yoshida, Kenta Urano, Shunsuke Aoki, Takuro Yonezawa, Nobuo Kawaguchi

ICMU page： 1 - 8 2021

RGNet: Robust Gravity Estimation Neural Network for IMU-based Localization Using Smartphone Reviewed

Yoshida, T; Urano, K; Aoki, S; Yonezawa, T; Kawaguchi, N

13TH INTERNATIONAL CONFERENCE ON MOBILE COMPUTING AND UBIQUITOUS NETWORK (ICMU2021) 2021

Estimating and Leveraging Latent Social Demand Based on IoT sensors: An Empirical Study in a Large Public Park. Reviewed

Yoshiteru Nagata, Daichi Murai, Shin Katayama, Kenta Urano, Shunsuke Aoki, Takuro Yonezawa, Nobuo Kawaguchi

ICMU page： 1 - 8 2021

Estimating and Leveraging Latent Social Demand Based on IoT sensors: An Empirical Study in a Large Public Park Reviewed

Nagata, Y; Murai, D; Katayama, S; Urano, K; Aoki, S; Yonezawa, T; Kawaguchi, N

13TH INTERNATIONAL CONFERENCE ON MOBILE COMPUTING AND UBIQUITOUS NETWORK (ICMU2021) 2021

Science and Technologies for Smart Cities - 6th EAI International Conference, SmartCity360° Virtual Event, December 2-4, 2020, Proceedings Reviewed

SmartCity360° Vol. 372 2021

Time-resolved X-ray photoelectron diffraction using an angle-resolved time-of-flight electron analyzer Reviewed

Ang A.K.R., Fukatsu Y., Kimura K., Yamamoto Y., Yonezawa T., Nitta H., Fleurence A., Yamamoto S., Matsuda I., Yamada-Takamura Y., Hayashi K.

Japanese Journal of Applied Physics Vol. 59 ( 10 ) 2020.10

Language：Japanese Publishing type：Research paper (scientific journal) Publisher：Japanese Journal of Applied Physics

X-ray photoelectron diffraction (XPD) provides atomic resolution, element sensitive local structure information about the surfaces and interfaces of materials. In the work reported in this paper, a two-dimensional angle resolved time-of-flight (2DARTOF) system is used to perform time-resolved XPD experiments on epitaxial silicene. The two Si 2p peak components from the different atomic sites in the silicene layer allow extraction of the individual XPD patterns. Time-resolved measurements captured small angle shifts in the forward focusing peak, indicating laser-induced changes in the silicene structure. At 10 ns, the XPD patterns appear to relax back to the equilibrium state. This work demonstrates that 2DARTOF systems are well suitable for time-resolved XPD measurements.

DOI： 10.35848/1347-4065/abb57e

Scopus

CityFlow: Supporting Spatial-Temporal Edge Computing for Urban Machine Learning Applications Reviewed

Kawano, M; Yonezawa, T; Tanimura, T; Giang, NK; Broadbent, M; Lea, R; Nakazawa, J

3RD EAI INTERNATIONAL CONFERENCE ON IOT IN URBAN SPACE page： 3 - 15 2020

Language：Japanese Publishing type：Research paper (scientific journal) Publisher：EAI/Springer Innovations in Communication and Computing

A growing trend in smart cities is the use of machine learning techniques to gather city data, formulate learning tasks and models, and use these to develop solutions to city problems. However, although these processes are sufficient for theoretical experiments, they often fail when they meet the reality of city data and processes, which by their very nature are highly distributed, heterogeneous, and exhibit high degrees of spatial and temporal variance. In order to address those problems, we have designed and implemented an integrated development environment called CityFlow that supports developing machine learning applications. With CityFlow, we can develop, deploy, and maintain machine learning applications easily by using an intuitive data flow model. To verify our approach, we conducted two case studies: deploying a road damage detection application to help monitor transport infrastructure and an automatic labeling application in support of a participatory sensing application. These applications show both the generic applicability of our approach, and its ease of use; both critical if we wish to deploy sophisticated ML based applications to smart cities.

DOI： 10.1007/978-3-030-28925-6_1

Web of Science

Scopus

GANonymizer: Image Anonymization Method Integrating Object Detection and Generative Adversarial Network Reviewed

Tanimura, T; Kawano, M; Yonezawa, T; Nakazawa, J

3RD EAI INTERNATIONAL CONFERENCE ON IOT IN URBAN SPACE page： 109 - 121 2020

Language：Japanese Publishing type：Research paper (scientific journal) Publisher：EAI/Springer Innovations in Communication and Computing

Sharing and analyzing image data from ubiquitous urban cameras must enable us to understand and predict various contexts of the city. Meanwhile, since such image data always contains privacy data such as people and cars, we cannot easily share and analyze the data through the Internet for the viewpoint of privacy protection. As a result, most of urban image data are only kept/shared within the camera owners, or even discarded to reduce risks of privacy data leakage. To solve the privacy problem and accelerate sharing of urban image data, we propose GANonymizer that automatically detects and removes objects related to privacy from the urban images. GANonymizer combines two neural networks: (1) a network which detects objects related to privacy such as persons and cars in an input image using object detection network and (2) a network that removes the detected objects naturally as though they are not exist originally. Through our experiment of applying GANonymizer to urban video images, we confirmed that GANonymizer partially achieved natural removal of objects related to privacy.

DOI： 10.1007/978-3-030-28925-6_10

Web of Science

Scopus

Civic Crowdsensing Through Location-Aware Virtual Monsters. Reviewed

Takuro Yonezawa, Mina Sakamura, Nobuo Kawaguchi, Jin Nakazawa

Distributed, Ambient and Pervasive Interactions - 8th International Conference Vol. 12203 LNCS page： 464 - 476 2020

SelfGuard: semi-automated activity tracking for enhancing self-protection against the COVID-19 pandemic: poster abstract. Reviewed

Yuuki Nishiyama, Takuro Yonezawa, Kaoru Sezaki

SenSys '20: The 18th ACM Conference on Embedded Networked Sensor Systems(SenSys) page： 780 - 781 2020

Person-Flow Estimation with Preserving Privacy Using Multiple 3D People Counters. Reviewed

Yoshiteru Nagata, Takuro Yonezawa, Nobuo Kawaguchi

Science and Technologies for Smart Cities - 6th EAI International Conference(SmartCity360°) Vol. 372 page： 615 - 634 2020

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Papers - YONEZAWA Takuro