Faculty Profiles - KAWAGUCHI, Nobuo

Papers - KAWAGUCHI, Nobuo

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通過情報を活用した移動経路分析・応用手法の評価と応用

永田吉輝, 浦野健太, 米澤拓郎, 河口信夫

第78回UBI研究会 2023.5

滞在ビッグデータに基づくエリア分散表現の加法構成性の分析と活用

田村直樹, 浦野健太, 米澤拓郎, 河口信夫

第107回MBL研究会 2023.5

移動ビッグデータを用いた公共空間の価値評価手法

坂倉波輝, 米澤拓郎, 田村直樹, 永田吉輝, 庄子和之, 山口公平, 浦野健太, 河口信夫

第107回MBL研究会 2023.5

IRIS: Interpretable Rubric-Informed Segmentation for Action Quality Assessment

Matsuyama H., Kawaguchi N., Lim B.Y.

International Conference on Intelligent User Interfaces, Proceedings IUI page： 368 - 378 2023.3

Language：English Publishing type：Research paper (scientific journal) Publisher：International Conference on Intelligent User Interfaces, Proceedings IUI

AI-driven Action Quality Assessment (AQA) of sports videos can mimic Olympic judges to help score performances as a second opinion or for training. However, these AI methods are uninterpretable and do not justify their scores, which is important for algorithmic accountability. Indeed, to account for their decisions, instead of scoring subjectively, sports judges use a consistent set of criteria - rubric - on multiple actions in each performance sequence. Therefore, we propose IRIS to perform Interpretable Rubric-Informed Segmentation on action sequences for AQA. We investigated IRIS for scoring videos of figure skating performance. IRIS predicts (1) action segments, (2) technical element score differences of each segment relative to base scores, (3) multiple program component scores, and (4) the summed final score. In a modeling study, we found that IRIS performs better than non-interpretable, state-of-the-art models. In a formative user study, practicing figure skaters agreed with the rubric-informed explanations, found them useful, and trusted AI judgments more. This work highlights the importance of using judgment rubrics to account for AI decisions.

DOI： 10.1145/3581641.3584048

Scopus

CrowdFlowTransformer: Capturing Spatio-Temporal Dependence for Forecasting Human Mobility Reviewed

Choya T., Tamura N., Katayama S., Urano K., Yonezawa T., Kawaguchi N.

2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023 page： 496 - 501 2023.3

Authorship：Last author Language：English Publishing type：Research paper (international conference proceedings) Publisher：2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023

Crowd flow forecasting is expected to have a wide range of applications such as human resource allocation, guidance design, marketing, disaster mitigation and congestion prediction for avoiding epidemic such as COVID-19. Crowd flow forecasting is challenging because it requires considering both the task of capturing the temporal dependency of data and capturing the spatial dependence. To address these challenges, in this paper, we propose a mechanism for referencing time-series features that are important for forecasting and incorporating graph convolution into Transformer, and we introduce CrowdFlowTransformer(CF-Transformer), a deep learning model based on Google's Transformer framework captures the Spatio-temporal dependency of time series. CF-Transformer captures the time series dependency by extracting important local time series from the past time series, inputting them to the decoder of Transformer, and encoding critical features into the model's input. We adapted CF-Transformer to a real-world crowd flow dataset. We evaluated it by comparing its forecasting accuracy with conventional models, and the results demonstrate that our model outperforms the conventional models.

DOI： 10.1109/PerComWorkshops56833.2023.10150301

Scopus

異種空間間のコミュニケーションを実現するロボット型メタポータルMetaPoにおけるロボットハンド・インタラクション

渡辺圭貴, 林田望海, 浦野健太, 米澤拓郎, 河口信夫

インタラクション2023 2023.3

自律ネットワークにつながる自律移動ロボットを用いる映像サービス実現に向けた初期プロトタイプの開発

髙橋美乃里, 山本秀樹, 岩下将人, 金田悠作, Leon Wong, 浦野健太, 米澤拓郎, 河口信夫

電子情報通信学会　総合大会 2023.3

Coordinated autonomous networks for remote synchronized video services with the autonomous mobility robots - prelude implementation Reviewed

Yamamoto, HH; Iwashita, M; Kondo, N; Wong, L; Kaneta, Y; Urano, K; Yonezawa, T; Kawaguchi, N

2023 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE IN INFORMATION AND COMMUNICATION, ICAIIC page： 598 - 601 2023.2

A Survey of Ground Truth Measurement Systems for Indoor Positioning Reviewed

Yoshida Takuto, Kaji Katsuhiko, Ogiso Satoki, Ichikari Ryosuke, Uchiyama Hideaki, Kurata Takeshi, Kawaguchi Nobuo

Journal of Information Processing Vol. 31 ( 0 ) page： 15 - 20 2023.1

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

<p>Various types of indoor positioning methods have been proposed. Accordingly, various ground-truth measurement methods have also been used. The selection of the ground-truth measurement method is crucial when appropriately evaluating the indoor positioning methods. In recent years, data-driven methods have been actively proposed, making the ground-truth measurement even more critical. Most existing survey papers only focus on classifying indoor positioning methods and datasets for evaluation. Our survey paper investigates the methods to measure the ground truth. First, we list the primary examples of ground truth measurement systems and summarize their characteristics, such as the scale, accuracy, and frequency when collecting ground truth. Then, we classify them into the following five categories: OMC, ToF/MFPS, SLAM, Manual, and Others. Their advantages and disadvantages are also discussed based on four indices: accuracy, coverage, device cost, and labor cost. Finally, we discuss the appropriate way to select the ground-truth measurement system for quantitative evaluation. Our survey will be the guideline for selecting an appropriate ground-truth measurement system for indoor positioning and indoor navigation.</p>

DOI： 10.2197/ipsjjip.31.15

Scopus

CiNii Research

XR Communication System for Remote Control Wheelchairs Reviewed

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

IoT '22: Proceedings of the 12th International Conference on the Internet of Things page： 219 - 223 2022.11

Dialogue Response Generation for Text-Based Dialogue Systems with Emotion Regulation

KATAYAMA Shin, YONEZAWA Takuro, KAWAGUCHI Nobuo

Journal of Japan Society for Fuzzy Theory and Intelligent Informatics Vol. 34 ( 3 ) page： 568 - 578 2022.8

Language：English Publishing type：Research paper (scientific journal) Publisher：Japan Society for Fuzzy Theory and Intelligent Informatics

<p>Dialogue systems that handle emotions have been shown to improve user satisfaction and increase positive interactions, and are expected to play a role as a digital partner that accompanies humans. In order for dialogue systems to recognize user emotions and express their own emotions, emotion regulation methods that select appropriate emotions from the context of the dialogue are indispensable. In this paper, we propose a text-based dialogue framework that performs emotion regulation, which estimates appropriate emotions during response. Our proposed method estimates appropriate emotions by considering the semantic and emotional context of the user’s text, and generates dialogue responses with emotional expressions using a neural network. The effectiveness of the proposed method was demonstrated through automatic evaluation using an evaluation metrics for the quality of dialogue generation and human evaluation by 100 subjects collected through a crowdsourcing.</p>

DOI： 10.3156/jsoft.34.3_568

CiNii Research

Towards Mobility-related Law Search by Utilizing Relationship between Laws

KOMAMIZU Takahiro, TOYAMA Katsuhiko, KAWAGUCHI Nobuo, SANO Tomoya

JSAI Technical Report, Type 2 SIG Vol. 2022 ( SWO-057 ) page： 04 2022.8

MetaPo: A Robotic Meta Portal for Interspace Communication Reviewed

Yonezawa T., Hayashida N., Przybilla J., Kyono Y., Urano K., Kawaguchi N.

Proceedings - SIGGRAPH 2022 Posters 2022.7

Authorship：Last author Language：English Publishing type：Research paper (scientific journal) Publisher：Proceedings - SIGGRAPH 2022 Posters

We introduce MetaPo, a mobile robot with spheric display, 360° media I/O and robotic hands for creating a unified model of interspace communication. MetaPo works as a portal between pairs of physical-physical, cyber-cyber and cyber-physical spaces to provide 1) panoramic communication for multiple remote users, and 2) immersive interspace migration with mobility functionality. The paper overviews our concept and first prototype of MetaPo with its hardware and software implementation.

DOI： 10.1145/3532719.3543255

Scopus

Smartphone Localization with Solar-Powered BLE Beacons in Warehouse Reviewed

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

DISTRIBUTED, AMBIENT AND PERVASIVE INTERACTIONS, SMART ENVIRONMENTS, ECOSYSTEMS, AND CITIES Vol. 13325 page： 291 - 310 2022.6

Authorship：Last author Language：English Publishing type：Research paper (scientific journal) Publisher：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Workloads in logistics warehouses have been increasing to meet growing demand, and a labor shortage has become a problem. Utilizing information of laborer locations leads to an increase in productivity. We propose an integrated positioning method using solar-powered Bluetooth Low Energy (BLE) beacons. They are easy to install and maintenance-free since they can work without power sources. However, their advertisement interval depends on illuminance and is unstable. Moreover, there are many obstructions in warehouses, such as shelves and products, which cause signal attenuation, interference, and packet losses. We apply particle filters, map matching, and speed prediction with a neural network model to improve robustness and accuracy. We installed 94 beacons in a logistics warehouse. We evaluated the accuracy and found that our method is more accurate than a baseline method.

DOI： 10.1007/978-3-031-05463-1_21

Web of Science

Scopus

Time-Sensitive Cooperative Perception for Real-Time Data Sharing over Vehicular Communications: Overview, Challenges, and Future Directions Invited Reviewed International coauthorship

Shunsuke Aoki, Takuro Yonezawa, Nobuo Kawaguchi, Peter Steenkiste, Ragunathan Raj Rajkumar

IEEE Internet of Things Magazine 2022.6

大規模屋外施設におけるWi-Fiパケットセンサへの影響と利活用の検証 Reviewed

村井大地, 浦野健太, 望月祐洋, 米澤拓郎, 西田純二, 河口信夫

情報処理学会デジタルプラクティス Vol. 3 ( 2 ) page： 12 - 21 2022.4

機械学習による慣性センサを用いた転倒動作検知に関する研究

東浦圭亮，吉田拓人，加納一馬，瀧上昂希，山口公平，浦野健太，青木俊介，米澤拓郎，河口信夫

社会システムと情報技術研究ウィーク (WSSIT2022) 2022.3

RobotNEST: Toward a Viable Testbed for IoT-Enabled Environments and Connected and Autonomous Robots Reviewed

Aoki Shunsuke, Yonezawa Takuro, Kawaguchi Nobuo

IEEE SENSORS LETTERS Vol. 6 ( 2 ) 2022.1

Authorship：Last author Language：English Publishing type：Research paper (scientific journal) Publisher：IEEE Sensors Letters

Autonomous mobile robots (AMRs), including delivery robots, security robots, and automated wheelchairs, are very promising for improving the quality of life, safety, mobility, and productivity. To deploy such sensor-rich AMRs in real-world society, we have to resolve not only technical challenges but also ethical and societal issues. In addition, since multiple companies are developing different types of AMRs individually, we have to design and develop practical platform to enable safe cooperation and collaboration before using them in the society. Hence, in this letter, we present a viable Nagoya Experimental Site and Testbed named <italic>RobotNEST</italic> developed at Nagoya City, Japan, where we leverage vehicular communications and edge/cloud computing technologies to develop cooperative mobile robots. RobotNEST provides real-world AMRs, experimental fields, learning kits, and infrastructural sensors and communications, including 5G private networks. We also introduce multiple developed applications at RobotNEST that enable safe cooperation for autonomous robots. Finally, we discuss the scalability and limitations of the testbed and conclude with future research directions.

DOI： 10.1109/LSENS.2021.3139624

Web of Science

Scopus

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

Tamura N., Urano K., Aoki S., Yonezawa T., Kawaguchi N.

Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST Vol. 419 LNICST page： 553 - 567 2022

Authorship：Last author Language：English Publishing type：Research paper (scientific journal) Publisher：Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

Recently, there has been an increasing demand for traffic simulation and congestion prediction for urban planning, especially for infection simulation due to the Covid-19 epidemic. On the other hand, the widespread use of wearable devices has made it possible to collect a large amount of user location history with high accuracy, and it is expected that this data will be used for simulation. However, it is difficult to collect location histories for the entire population of a city, and detailed data that can reproduce trajectories is expensive. In addition, such personal location histories contain private information such as addresses and workplaces, which restricts the use of raw data. This paper proposes Agent2Vec, a mobility modeling model based on unsupervised learning. Using this method, we generate synthetic human flow data without personal information.

DOI： 10.1007/978-3-030-94822-1_36

Scopus

A Data-Driven Approach for Online Pre-impact Fall Detection with Wearable Devices Reviewed

Yoshida Takuto, Kano Kazuma, Higashiura Keisuke, Yamaguchi Kohei, Takigami Koki, Urano Kenta, Aoki Shunsuke, Yonezawa Takuro, Kawaguchi Nobuo

SENSOR- AND VIDEO-BASED ACTIVITY AND BEHAVIOR COMPUTING, ABC 2021 Vol. 291 page： 133 - 147 2022

Authorship：Last author Language：English Publishing type：Research paper (scientific journal) Publisher：Smart Innovation, Systems and Technologies

The implementation of wearable airbags to prevent fall injuries depends on accurate pre-impact fall detection and a clear distinction between activities of daily living (ADL) and them. We propose a novel pre-impact fall detection algorithm that is robust against ambiguous falling activities. We present a data-driven approach to estimate the fall risk from acceleration and angular velocity features and use thresholding techniques to robustly detect a fall before impact. In the experiment, we collect simulated fall data from subjects wearing an inertial sensor on their waist. As a result, we succeeded in significantly improving the accuracy of fall detection from 50.00 to 96.88%, the recall from 18.75 to 93.75%, and the specificity 81.25 to 100.00% over the baseline method.

DOI： 10.1007/978-981-19-0361-8_8

Web of Science

Scopus

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Papers - KAWAGUCHI, Nobuo