研究者詳細 - 河口　信夫

論文 - 河口　信夫

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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査読有り Open Access

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 3 巻 ( 2 ) 頁： 12 - 21 2022年4月

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年3月

RobotNEST: Toward a Viable Testbed for IoT-Enabled Environments and Connected and Autonomous Robots 査読有り

Aoki Shunsuke, Yonezawa Takuro, Kawaguchi Nobuo

IEEE SENSORS LETTERS 6 巻 ( 2 ) 2022年1月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：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 査読有り

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

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

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：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 査読有り

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 291 巻頁： 133 - 147 2022年

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：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

物流倉庫における人・配送ロボット協調のためのセンサ統合システム Open Access

河口信夫

情報処理学会研究報告 (MBL) 2022-MBL-104 巻頁： 1 - 8 2022年

大規模移動履歴に基づく滞在推定結果を用いた公園の滞在特徴の分類手法 Open Access

河口信夫

情報処理学会第104回MBL研究会 2022-MBL-104 巻頁： 1 - 6 2022年

3次元LiDARを搭載した自律走行ロボットを用いたWi-Fi電波強度および通信速度測定システム

盛下泰暉, 浅井悠佑, 浦野健太, 米澤拓郎, 河口信夫

情報処理学会研究報告 2021-MBL-101 巻 ( 34 ) 頁： 1 - 6 2021年12月

スマートメータを利用した行動認識のための電気使用量クラスタリングに関する検討

深谷暢也，浦野健太，青木俊介, 米澤拓郎, 河口信夫

令和3年度電気・電子・情報関係学会東海支部連合 2021年9月

How to monitor multiple autonomous vehicles remotely with few observers: An active management method

Ding M., Takeuchi E., Ishiguro Y., Ninomiya Y., Kawaguchi N., Takeda K.

IEEE Intelligent Vehicles Symposium, Proceedings 2021-July 巻頁： 1168 - 1173 2021年7月

記述言語：日本語掲載種別：研究論文（学術雑誌）出版者・発行元：IEEE Intelligent Vehicles Symposium, Proceedings

In this research, we proposed an active management method to tele-monitor and tele-operate more autonomous vehicles (AVs) with few observers by adjusting the movement of the AVs actively. A management system is created to get the status from the AVs and separate the monitoring requirement to the observers optimally. When the requirements might be intensive, the management system can also adjust the movements of the AVs actively to distribute the monitoring time, which can make the observers monitor more vehicles. We implement and verify the management system in an autonomous driving simulator - CARLA for the limited number of AVs and observers. Based on the data acquired from the driving simulator, we also create a numerical simulator and tested our method with the pairs of a large number of AVs and observers. The result of both simulators shows that our method can reduce the utilization degree of observers and make them monitor more AVs.

DOI： 10.1109/IV48863.2021.9575997

Scopus

都市を対象とした大規模移動履歴に基づく疑似人流データ生成手法 Open Access

田村直樹, 浦野健太, 青木俊介, 米澤拓郎, 河口信夫