研究者詳細 - 河口　信夫

論文 - 河口　信夫

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Editor's Message to Special Issue of Pervasive System and Intelligent Transportation Systems for Future Life. 査読有り

Nobuo Kawaguchi

JIP 27 巻 ( 0 ) 頁： 1 - 1 2019年

A smartphone 3D positioning method using a spinning magnet marker 査読有り

Watanabe K., Hiroi K., Kamiyama T., Sano H., Tsukamoto M., Katagiri M., Ikeda D., Kaji K., Kawaguchi N.

Journal of Information Processing 27 巻 ( 0 ) 頁： 10 - 24 2019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

In recent years, the importance of location information has increased due to the popularization of terminals such as smartphones. Our purpose is to estimate the 3D position of smartphones within several centimeters. This location information can reveal a person’s behavior patterns and subject of interest. A method based on dynamic magnetism can estimate the 3D position of a terminal within several centimeters, and it is robust against the environment. However, since the performance of the smartphone’s magnetic sensor is limited, this method cannot be used in smartphones. In this research, we propose a smartphone 3D positioning method using a Spinning Magnet Marker (SMM) that spins a powerful neodymium magnet with a motor. Magnetoquasistatic field generated by an SMM is detected by a magnetic sensor mounted on a smartphone, and the 3D position of a smartphone based on the SMM is estimated from the magnetism. We developed an SMM with a stepper motor to improve estimation accuracy. In addition, we studied the magnetism generated by an SMM and derive equations to estimate the 3D position of a smartphone. Furthermore, we improved the estimation accuracy and expanded coverage by introducing a noise reduction method. We evaluate the estimation accuracy of the proposed method. When the azimuth angle is 0 ◦ , the elevation angle is 0 ◦ , and the distance is 3 m, the azimuth angle is estimated with a mean error of 1 ◦ , the elevation angle is estimated with a mean error of 4 ◦ , and the distance is estimated with a mean error of 9 cm.

DOI： 10.2197/ipsjjip.27.10

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CiNii Research

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Ballroom Dance Step Type Recognition by Random Forest Using Video and Wearable Sensor

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

UBICOMP/ISWC'19 ADJUNCT: PROCEEDINGS OF THE 2019 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING AND PROCEEDINGS OF THE 2019 ACM INTERNATIONAL SYMPOSIUM ON WEARABLE COMPUTERS 頁： 774-780 2019年

配布型BLEタグとタンデムスキャナを用いた屋内位置推定手法査読有り

浦野健太, 廣井慧, 梶克彦, 河口信夫

情報処理学会論文誌 60 巻頁： 58 - 75 2019年

Road Surface Condition Inspection Using a Laser Scanner Mounted on an Autonomous Driving Car. 査読有り

Kenta Urano, Kei Hiroi, Shinpei Kato, Nozomi Komagata, Nobuo Kawaguchi

IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019, Kyoto, Japan, March 11-15, 2019 頁： 826 - 831 2019年

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：IEEE

Inspection and repair of road infrastructures are important for safety. While highways and motorways are periodically inspected with specialized vehicles, the roads which are maintained by local governments are not inspected because of lack of budget and workforce. In the future, however, a large number of autonomous driving cars will run everywhere. They are equipped with laser scanners to recognize the surroundings. If we utilize these sensors to inspect the road surface condition automatically, the cost of inspection will be reduced dramatically. In this paper, we extract transverse profiles of a road using point clouds which are recorded by a laser scanner mounted on an autonomous driving car. Point clouds are converted to the same format of special vehicle. We compared the data recorded by a specialized vehicle and a car with a laser scanner in Ichinomiya, Aichi prefecture, Japan and found that proposed method can be used as a preliminary survey to find where needs the detailed inspection.

DOI： 10.1109/PERCOMW.2019.8730820

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Hybrid Activity Recognition for Ballroom Dance Exercise using Video and Wearable Sensor

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

2019 JOINT 8TH INTERNATIONAL CONFERENCE ON INFORMATICS, ELECTRONICS & VISION (ICIEV) AND 2019 3RD INTERNATIONAL CONFERENCE ON IMAGING, VISION & PATTERN RECOGNITION (ICIVPR) WITH INTERNATIONAL CONFERENCE ON ACTIVITY AND BEHAVIOR COMPUTING (ABC) 頁： 112 - 117 2019年

Road Surface Condition Inspection Using a Laser Scanner Mounted on an Autonomous Driving Car

Urano Kenta, Hiroi Kei, Kato Shinpei, Komagata Nozomi, Kawaguchi Nobuo

2019 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS (PERCOM WORKSHOPS) 頁： 826 - 831 2019年

Gait Dependency of Smartphone Walking Speed Estimation using Deep Learning. 査読有り

Takuto Yoshida, Junto Nozaki, Kenta Urano, Kei Hiroi, Takuro Yonezawa, Nobuo Kawaguchi

Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services, MobiSys 2019, Seoul, Republic of Korea, June 17-21, 2019. 頁： 641 - 642 2019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：ACM

This paper proposes an accurate estimation method of walking speed using deep learning for smartphone-based Pedestrian Dead Reckoning (PDR). PDR requires to estimate speed and direction of pedestrians accurately using accelerometer and gyroscope. To improve the accuracy of PDR, existing works focused to improve the key factors of speed estimation (i.e., stride and/or step estimation) by adapting deep learning. On the contrary, our research proposes to adapt deep learning more directly to estimate walking speed from sensor data of smartphone. We evaluate the accuracy of proposed method by comparing with conventional PDR method. As a result, we confirmed that proposed method can estimate the speed more accurately.

DOI： 10.1145/3307334.3328667

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A Smartphone Short-Range Path Estimation with Hyperbolic Function for Spinning Magnet Marker. 査読有り

Kosuke Watanabe, Nobuo Kawaguchi

IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019, Kyoto, Japan, March 11-15, 2019 頁： 459 - 460 2019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：IEEE

Recently, an importance of location information has increased with the spread of smartphones. Our purpose is to estimate a smartphone position with a few centimeters error. In addition, we would like to recognize behavior patterns of people, and targets of their interest. These information will provide new services. For example, at an event venue, it will be possible to provide information about other exhibits according to their positions. There is a method that can estimate a position of a magnetic sensor with a few centimeters error is using dynamic magnetism. This method is available even if under strong environmental magnetism because it uses frequency of dynamic magnetism to positioning. However, it is difficult to apply these positioning methods to a smartphone, because a sampling frequency of a magnetic sensor mounted on a smartphone is dozen of hertz, whereas these methods use a dynamic magnetism with several kilohertz [1], [2]. Therefore, we developed a Spinning Magnetic Marker (SMM) which generates dynamic magnetism by spinning a strong magnet, and proposed a positioning method based on dynamic magnetism which can be applied to a smartphone [3], [4]. In this study, we propose a method to estimate a short-range path of a moving smartphone by the hyperbolic function, and curve fitting with magnetism equation.

DOI： 10.1109/PERCOMW.2019.8730728

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An End-to-End BLE Indoor Location Estimation Method Using LSTM

Urano Kenta, Hiroi Kei, Yonezawa Takuro, Kawaguchi Nobuo

2019 TWELFTH INTERNATIONAL CONFERENCE ON MOBILE COMPUTING AND UBIQUITOUS NETWORK (ICMU) 2019年

Basic Study of BLE Indoor Localization using LSTM-based Neural Network. 査読有り

Kenta Urano, Kei Hiroi, Takuro Yonezawa, Nobuo Kawaguchi

Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services, MobiSys 2019, Seoul, Republic of Korea, June 17-21, 2019. 頁： 558 - 559 2019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：ACM

In this paper, LSTM-based neural network is applied to indoor localization using mobile BLE tag’s signal strength collected by multiple scanners. Stability of signal strength is a critical factor of wireless indoor localization for higher accuracy. While traditional methods like trilateration and fingerprinting suffer from noise and packet loss, deep learning based methods perform well. We focus on large-scale exhibition where wireless signal gets unstable due to many people. Proposed neural network consists of fully connected layers for noise removal and LSTM layers for time-series feature extraction. The network takes the time-series of signal strength as input and outputs the estimated location. In the evaluation, the number of layers is changed to find the optimal structure. As a result, the best configuration achieved the error of 2.44m at 75 percentile for the data of a large-scale exhibition in Tokyo.

DOI： 10.1145/3307334.3328624

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Capturing Subjective Time as Context and It's Applications. 査読有り

Takuro Yonezawa, Yuuki Nishiyama, Kei Hiroi, Nobuo Kawaguchi

Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services, MobiSys 2019, Seoul, Republic of Korea, June 17-21, 2019. 頁： 647 - 648 2019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：ACM

We propose an integrated framework for sensing, recognizing and utilizing of subjective time as context. Various studies on experimental psychology have showed several factors which affects subjective time. Those factors should be partially captured by ubiquitous sensors such as smartphones and wearable devices, therefore, we tackle to create common and individual model for subjective time based on the sensor data. We report our first prototype implementation for the framework based on AWARE framework with adding experience sampling method for subjective time recognition. In addition, we discuss potential applications which leveraging advantages of subjective time as context.

DOI： 10.1145/3307334.3328719

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Compensation Scheme for PDR Using Component-Wise Error Models 査読有り

Nozaki Junto, Hiroi Kei, Kaji Katsuhiko, Kawaguchi Nobuo

HUMAN ACTIVITY SENSING: CORPUS AND APPLICATIONS 頁： 29 - 46 2019年

End-to-end walking speed estimation method for smartphone PDR using DualCNN-LSTM

Kawaguchi N., Nozaki J., Yoshida T., Hiroi K., Yonezawa T., Kaji K.

CEUR Workshop Proceedings 2498 巻頁： 463 - 470 2019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：CEUR Workshop Proceedings

Most of the current inertial positioning systems can be categorized as the strapdown algorithm and the step-and-heading approach. However, for the strapdown algorithm using smartphone as a sensor device, the accuracy of the current MEMS based accelerometer are not enough for estimating relative movement. Also, for the step-and-heading approach, robust estimation of step length is always difficult. In this paper, we propose an end-to-end walking speed estimation method using Deep Learning to overcome these problems. By using our method, we can achieve a smartphone PDR with higher accuracy and better robustness to gait type.

Scopus

Human Activity Sensing Corpus and Applications Preface

Kawaguchi Nobuo, Nishio Nobuhiko, Roggen Daniel, Inoue Sozo, Pirttikangas Susanna, Van Laerhoven Kristof

HUMAN ACTIVITY SENSING: CORPUS AND APPLICATIONS 頁： V - V 2019年

An End-to-End BLE Indoor Location Estimation Method Using LSTM

Urano Kenta, Hiroi Kei, Yonezawa Takuro, Kawaguchi Nobuo

2019 TWELFTH INTERNATIONAL CONFERENCE ON MOBILE COMPUTING AND UBIQUITOUS NETWORK (ICMU) 2019年

A Slope-robust Cascaded Ground Segmentation in 3D Point Cloud for Autonomous Vehicles

Narksri Patiphon, Takeuchi Eijiro, Ninomiya Yoshiki, Morales Yoichi, Akai Naoki, Kawaguchi Nobuo

2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) 頁： 497-504 2018年

Activity Recognition Using Dual-ConvLSTM Extracting Local and Global Features for SHL Recognition Challenge

Yuki Yuta, Nozaki Junto, Hiroi Kei, Kaji Katsuhiko, Kawaguchi Nobuo

PROCEEDINGS OF THE 2018 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING AND PROCEEDINGS OF THE 2018 ACM INTERNATIONAL SYMPOSIUM ON WEARABLE COMPUTERS (UBICOMP/ISWC'18 ADJUNCT) 頁： 1643-1651 2018年

IMAGE TRANSLATION BETWEEN SAR AND OPTICAL IMAGERY WITH GENERATIVE ADVERSARIAL NETS

Enomoto Kenji, Sakurada Ken, Wang Weiming, Kawaguchi Nobuo, Matsuoka Masashi, Nakamura Ryosuke

IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM 頁： 1752-1755 2018年

Image-Matching Based Identification of Store Signage Using Web-Crawled Information

Liao Chenyi, Wang Weimin, Sakurada Ken, Kawaguchi Nobuo

IEEE ACCESS 6 巻頁： 45590-45605 2018年

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