Faculty Profiles - KAWAGUCHI, Nobuo

Papers - KAWAGUCHI, Nobuo

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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

河口信夫

情報処理学会研究報告 (MBL) Vol. 2022-MBL-104 page： 1 - 8 2022

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

河口信夫

情報処理学会第104回MBL研究会 Vol. 2022-MBL-104 page： 1 - 6 2022

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 Vol. 2021-MBL-101 ( 34 ) page： 1 - 6 2021.12

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 2021.9

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 Vol. 2021-July page： 1168 - 1173 2021.7

Language：Japanese Publishing type：Research paper (scientific journal) Publisher：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

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

田村直樹, 浦野健太, 青木俊介, 米澤拓郎, 河口信夫

マルチメディア、分散、協調とモバイル DICOMO2021シンポジウム 2021.7

自律移動ロボットのセンサ機器を用いた人流推定手法の提案

下里浩昇, 片山晋, 浦野健太, 青木俊介, 米澤拓郎, 河口信夫

マルチメディア、分散、協調とモバイル DICOMO2021シンポジウム 2021.7

大規模公園環境におけるWiFiパケットセンサデータの利活用に関する分析と課題

村井大地, 浦野健太, 青木俊介, 米澤拓郎, 河口信夫

マルチメディア、分散、協調とモバイル DICOMO2021シンポジウム 2021.7

IoTに基づく潜在的社会需要の推定と柔軟なサービス需給交換基盤

永田吉輝, 村井大地, 片山晋, 浦野健太, 青木俊介, 米澤拓郎, 河口信夫

マルチメディア、分散、協調とモバイル DICOMO2021シンポジウム 2021.7

時空間ルーティングを用いた複数自律移動ロボットの協調走行

福島悠人, 浅井悠佑, 浦野健太, 青木俊介, 米澤拓郎, 河口信夫

マルチメディア、分散、協調とモバイル DICOMO2021シンポジウム 2021.7

赤外線グリッドセンサを用いた深層学習での人の位置推定手法の検討

戸出悠太, 片山晋, 浦野健太, 青木俊介, 米澤拓郎, 河口信夫

マルチメディア、分散、協調とモバイル DICOMO2021シンポジウム 2021.7

Transformerモデルを用いた人流の時系列予測

挺屋友幹, 片山晋, 浦野健太, 青木俊介, 米澤拓郎, 河口信夫

マルチメディア、分散、協調とモバイル DICOMO2021シンポジウム 2021.7

加速度の時空間情報を考慮した進行方向推定手法の検討

吉田拓人, 浦野健太, 青木俊介, 米澤拓郎, 河口信夫

マルチメディア、分散、協調とモバイル DICOMO2021シンポジウム 2021.7

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

田村直樹, 浦野健太, 青木俊介, 米澤拓郎, 河口信夫

マルチメディア、分散、協調とモバイル DICOMO2021シンポジウム 2021.7

時間・空間的相関を考慮した人流の時系列予測手法

挺屋友幹, 青木俊介, 米澤拓郎, 河口信夫

研究報告ユビキタスコンピューティングシステム（UBI） 2021.3

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：Japanese 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

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 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

An Active Management Method for Monitoring Multiple Autonomous Vehicles Remotely with Few Observers

DING Ming, Takeuchi Eijiro, ISHIGURO Yoshio, NINOMIYA Yoshiki, KAWAGUCHI Nobuo, TAKEDA Kazuya

The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) Vol. 2021 ( 0 ) page： 2P1-B10 2021

Language：Japanese Publishing type：Research paper (scientific journal) Publisher：The Japan Society of Mechanical Engineers

<p>In this research, we proposed an active management method to monitor more autonomous vehicles (AVs) remotely with few observers. 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 adjust the movements of the AVs actively to distribute the monitoring time, which can make the observers monitor more vehicles. Based on the data acquired from the driving simulator, we create a numerical simulator and test our method with different pairs of a large number of AVs and observers. The result showed that our method can reduce the utilization degree of observers and make them monitor more AVs.</p>

DOI： 10.1299/jsmermd.2021.2p1-b10

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, ICMU 2021 2021

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

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

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

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