研究者詳細 - 河口　信夫

論文 - 河口　信夫

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An end-to-end ble indoor localization method using lstm

Urano K, Hiroi K, Yonezawa T, Kawaguchi N

Journal of Information Processing 29 巻 ( 0 ) 頁： 58 - 69 2021年

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：Journal of Information Processing

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 1.92 m, using the data collected in GEXPO exhibition in Miraikan, Tokyo. This paper also evaluates the estimation accuracy assuming the troubles in a real operation.

DOI： 10.2197/ipsjjip.29.58

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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

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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

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

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

13th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2021 頁： 1 - 8 2021年

The continuously changing conditions of cities are now technically understandable in the information space through real-world sensing methods and analytical methods such as big data analysis and machine learning. On the other hand, it is currently difficult to estimate and present what the people in the city need (latent demand). This paper aims to solve latent demand by developing Latent Demand Resolver (LD-Resolver), the new latent demand estimation and exchanging system. LD-Resolver has two components, Latent Demand Extractor (LD-Extractor) and Latent Demand Exchanger (LD-Exchanger). LD-Extractor extracts a latent demand from various social conditions using IoT sensors, Web, and SNS. LD-Exchanger has a new structure to exchange a latent demand with an appropriate service supply. Finally, we developed the LD-Resolver and conducted a demonstration experiment at the Higashiyama Zoo and Botanical Garden to verify the method. As a result of the two-week experiment, the proposed method can be effectively used in actual facility operations.

DOI： 10.23919/ICMU50196.2021.9638789

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DigiMobot: Digital Twin for Human-Robot Collaboration in Indoor Environments 査読有り

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

2021 32ND IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV) 2021-July 巻頁： 55 - 62 2021年

担当区分：最終著者記述言語：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

Human-robot collaboration and cooperation are critical for Autonomous Mobile Robots (AMRs) in order to use them in indoor environments, such as offices, hospitals, libraries, schools, factories, and warehouses. Since a long transition period might be required to fully automate such facilities, we have to deploy AMRs while improving safety in the mixed environments of human and mobile robots. In addition, human behaviors in such environments might be difficult to predict. In this paper, we present a Digital Twin for Autonomous Mobile Robots system named DigiMobot to support, manage, monitor, and validate AMRs in indoor environments. First, DigiMobot can simulate human behaviors and robot movements to verify and validate AMRs to improve safety in a virtual world. Secondly, DigiMobot can monitor and manage AMRs in the physical world by collecting sensor data from each robot in real-time. Since DigiMobot enables us to test the robot systems in the virtual world, we can deploy and implement AMRs in each facility without any modifications. To show the feasibility of DigiMobot, we develop a software framework and two different types of autonomous mobile robots. Finally, we conduct real-world experiments in a warehouse located in Saitama, Japan, in which more than 400, 000 items are stored for commercial purposes.

DOI： 10.1109/IV48863.2021.9575499

Web of Science

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A Gaze-Based Unobstructive Information Selection by Context-Aware Moving UI in Mixed Reality 査読有り

Hayashida N, Matsuyama H, Aoki S, Yonezawa T, Kawaguchi N

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 12782 LNCS 巻頁： 301 - 315 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 in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Mixed reality (MR) technology has been attracting attention in the automobile industry and logistics industry for work training and remote work support for newcomers. However, work support using MR technology has the problem that displaying too much information obstructs the user’s field of vision and rather interferes with the work. Therefore, it is necessary to detect the user’s intention and provide only the information that the user wants. In addition, the system should be able to detect naturally from the user’s behavior without interrupting the work, which the user explicitly selects. To solve these problems, in this paper, we use the user’s gaze to determine whether to display content by estimating whether or not they are looking at information. Another problem with gaze-based research in MR work support is that it is difficult to determine which space you are looking at in a space where virtual space and real space intersect. In order to overcome this problem, in this research, we grasp the user’s behavior from the movement of the user’s gaze, and we suggest a way to determine make the user interface (UI) the movement of the gaze that is unlikely to occur during that behavior to see if the user is looking at the UI. As a result of the experiment, we were able to promote the movement of gaze that is different from the characteristics of the movement of gaze during work with the proposed UI movement.

DOI： 10.1007/978-3-030-77015-0_22

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

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

Smart Innovation, Systems and Technologies 204 巻頁： 209 - 226 2021年

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：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

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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 Open Access

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

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