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Person-Flow Estimation with Preserving Privacy Using Multiple 3D People Counters 査読有り

Nagata Y., Yonezawa T., Kawaguchi N.

Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST 372 巻頁： 615 - 634 2021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

The spread of mobile phones made it easy to estimate person-flow for corporate marketing, crowd analysis, and countermeasures for disaster and disease. However, due to recent privacy concerns, regulations have been tightened around the world and most smartphone operating systems have increased privacy protection. To solve this, in this study, we propose the person-flow estimation technique with preserving privacy. We use 3D People Counter which can record only the time and direction of passing people, a person’s height, and walking speed, therefore it preserves privacy from the moment of collecting data. To estimate people’s in-out data, we propose four methods and they use some of the sensor data above in different combinations. We compared these methods and the height-based method could estimate about 79% of the sensor data as in-out data. Additionally, we also created a system to interpolate in-out data into person-flow data and to visualize it. By using this method, we believe that it can be used for the purposes described in the beginning.

DOI： 10.1007/978-3-030-76063-2_41

Scopus

複数の自動運転車両を少人数で遠隔監視するための能動的管理手法

丁明, 竹内栄二郎, 石黒洋生, 二宮芳樹, 河口信夫, 武田一哉

ロボティクス・メカトロニクス講演会講演概要集 2021 巻 ( 0 ) 頁： 2P1-B10 2021年

RGNet: Robust Gravity Estimation Neural Network for IMU-based Localization Using Smartphone 査読有り

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

13th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2021 2021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：13th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2021

With the rapid development of Micro Electro-Mechanical Systems (MEMS) technologies, indoor navigation and localization with Inertial Measurement Unit (IMU) has been increasingly feasible. IMU-based indoor localization is a low-cost, energy-efficient, and infrastructure-free approach. There are various methods for it, and most of them require gravity estimation (e.g. the projection of angular velocity, the extraction of horizontal acceleration). In particular, when you use a smartphone, it changes the orientation of its IMU frequently, therefore the gravity estimation needs to be more robust to sensor orientation and its noise. In this paper, we propose a gravity estimation method based on deep learning called RGNet (Robust Gravity Estimation Neural Network) that is robust to sensor orientation and noise. We train an LSTM (Long short-term memory)-based neural network that estimates gravity from acceleration and angular velocity. Furthermore, for the problem that it is difficult to prepare the ground truth of gravity directly, we propose a method to train the gravity estimation neural network indirectly using the heading, taking advantage of the fact that the heading can be estimated from the gravity and angular velocity. The evaluation results show that the accuracy of the proposed method outperformed the baseline gravity estimation method (Android API, Low-pass filter, and Extended Kalman filter). We also confirmed that the accuracy of IMU-based localization is affected by the difference in gravity estimation methods.

DOI： 10.23919/ICMU50196.2021.9638853

Scopus

ER-Chat: 感情制御を伴う対話システムにおけるテキスト応答生成手法

片山晋, 米澤拓郎, 大越匡, 中澤仁, 河口信夫

情報処理学会研究報告ユビキタスコンピューティングシステム(UBI) 2020-UBI-68 No.26 巻 2020年12月

感情制御を伴うテキスト対話システムの実現

片山晋, 米澤拓郎, 大越匡. 中澤仁. 河口信夫

人間情報学会オーラルセッション第37回巻 2020年12月

Person-Flow Estimation with Preserving Privacy using Multiple 3D People Counters 査読有り

Yoshiteru Nagata, Takuro Yonezawa, Nobuo Kawaguchi

Transaction of 5th EAI International Conference on IoT in Urban Space 2020年12月

Autonomous Mobile Robot System for Human Machine Harmonization using Harmoawre 招待有り

Nobuo Kawaguchi

IEEE InTech Forum 2020年12月

COVID-19における人流分析とHarmoware-VISによる可視化招待有り

河口信夫

電子情報通信学会 SeMI 研究会 2020年11月

COVID-19における人流分析とHarmoware-VISによる可視化招待有り

河口信夫

電子情報通信学会 SeMI 研究会 2020年11月

社交ダンスの動作特性を考慮したマルチモーダルセンサによるダンスフィガー認識査読有り

松山仁, 浦野健太，廣井慧, 梶克彦, 米澤拓郎, 河口信夫

情報処理学会論文誌 Vol. 61 巻 ( No. 10 ) 2020年10月

A Basic Study on Ballroom Dance Figure Classification with LSTM using Multi-modal Sensor 査読有り

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

2nd International Conference on Activity and Behavior Computing 2020年9月

分散型人流・交通流シミュレータの大規模環境における動作実証

平野流，米澤拓郎，河口信夫

電気・電子・情報関係学会東海支部連合大会 2020年9月

エリア毎の滞在利用情報を用いた移動遷移モデルの分析

庄子和之，米澤拓郎，河口信夫

電気・電子・情報関係学会東海支部連合大会 2020年9月

Wi-Fiパケットセンサを用いた大規模レジャー施設における人流分析

村井大地，米澤拓郎，河口信夫

電気・電子・情報関係学会東海支部連合大会 2020年9月

Civic Crowdsensing through Location-aware Virtual Monsters 査読有り

Takuro Yonezawa, Mina Sakamura, Jin Nakazawa, Nobuo Kawaguchi

22nd INTERNATIONAL CONFERENCE ON HUMAN-COMPUTER INTERACTION (HCII 2020) 2020年7月

Civic Crowdsensing Through Location-Aware Virtual Monsters 査読有り

Yonezawa T., Sakamura M., Kawaguchi N., Nakazawa J.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 12203 LNCS 巻頁： 464 - 476 2020年7月

担当区分：最終著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

We present a new model for encouraging people to get involved with monitoring and taking part in the life of cities. Cities could be smarter if IoT and people could serve as engaged and pro-active data resources (i.e., crowd sensing). This study tackles two challenges: methods by which the privacy of people who act as sensors/actuators can be guaranteed and methods to create a unified programming model for crowd sensors alongside other IoT functions. To achieve these goals, we introduce a new concept called Lokemon (Location Monster). Each sensing space is characterized as a personified target. Lokemon asks users to imagine themselves to be monsters associated with target spots when achieving sensing tasks. Lokemon is also expressed as a PubSub node so that the data from Lokemon can be easily accessed in the same way as data from IoT is assessed. The article explains the concept of Lokemon and its programming model. We report our evaluation of the effectiveness of Lokemon in a campus experiment that was performed for four weeks.

DOI： 10.1007/978-3-030-50344-4_33

Scopus

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その他リンク： https://dblp.uni-trier.de/db/conf/hci/hci2020-23.html#YonezawaSKN20

Nurse Care Activity Recognition Challenge: A Comparative Verification of Multiple Preprocessing Approaches 査読有り

Hitoshi Matsuyama, Takuto Yoshida, Nozomi Hayashida, Yuto Fukushima, Takuro Yonezawa, Nobuo Kawaguchi

UbiComp-ISWC '20: Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computer 頁： 414 2020年7月

人と協調する倉庫内配送ロボットのシミュレーションを用いた最適化の検討

浅井悠佑, 廣井慧, 米澤拓郎, 河口信夫

マルチメディア、分散、協調とモバイル DICOMO2020シンポジウム 2020年6月

大規模分散環境における人流・交通流シミュレータの設計と課題

平野流, 廣井慧, 米澤拓郎, 河口信夫

マルチメディア、分散、協調とモバイル DICOMO2020シンポジウム 2020年6月

複数の3D通過センサによるプライバシーを考慮した人流推定手法

永田吉輝, 廣井慧, 米澤拓郎, 河口信夫

マルチメディア、分散、協調とモバイル DICOMO2020シンポジウム 2020年6月

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