研究者詳細 - 河口　信夫

論文 - 河口　信夫

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Anomaly Event Detection using Bus Management Information -Case Study of Anomaly Operation Status Detection and Its Application 査読有り

Wei Sun, Kei Hiroi, Takuro Yonezawa, Nobuo Kawaguchi

( 2019-ITS-79(5) ) 頁： 1 - 5 2019年11月

An End-to-End BLE Indoor Location Estimation Method using LSTM 査読有り

Kenta Urano, Kei Hiroi, Takuro Yonezawa, Nobuo Kawaguchi

ICMU2019 2019年11月

An End-to-End BLE Indoor Location Estimation Method using LSTM 査読有り

Kenta Urano, Kei Hiroi, Takuro Yonezawa, Nobuo Kawaguchi

ICMU2019 2019年11月

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

Urano K, Hiroi K, Yonezawa T, Kawaguchi N

2019 12th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2019 2019年11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：2019 12th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2019

Indoor location estimation has long been researched to realize location-based services. In this paper, we propose an indoor location estimation method for Bluetooth Low Energy (BLE) devices using end-to-end LSTM neural network. We focus on large-scale exhibition where is a tough environment for wireless indoor location estimation due to signal strength instability. To achieve higher accuracy, deep learning based methods are proposed rather than trilateration or fingerprint. Existing deep learning based methods estimate the location from the probabilities using the difference of query signal strength and autoencoder-reconstruction of it. Proposed method adopts end-to-end location estimation, which means the neural network takes a time-series of signal strength and outputs the estimated location at the latest time in the input time-series. We also build a loss function which takes how a person walks into account. Considering the difficulty of data collection within a short preparation term of an exhibition, the data generated by a simple simulation is used in the training phase before training with a small amount of real data. As a result, the estimation accuracy is average of 1.92m, using the data collected in GEXPO exhibition in Miraikan, Tokyo. Proposed method outperforms our previous trilateration based method's 4.51m average.

DOI： 10.23919/ICMU48249.2019.9006638

Scopus

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Sampling Rate Dependency in Pedestrian Walking Speed Estimation using DualCNN-LSTM 査読有り

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

Ubicomp2019 2019年10月

End-to-End Walking Speed Estimation Method for Smartphone PDR using DualCNN-LSTM 査読有り

Nobuo Kawaguchi, Junto Nozaki, Takuto Yoshida, Kei Hiroi, Takuro Yonezawa, Katsuhiko Kaji

The Tenth International Conference on Indoor Positioning and Indoor Navigation(IPIN2019). 2019年9月

End-to-End Walking Speed Estimation Method for Smartphone PDR using DualCNN-LSTM 査読有り

Nobuo Kawaguchi, Junto Nozaki, Takuto Yoshida, Kei Hiroi, Takuro Yonezawa, Katsuhiko Kaji

The Tenth International Conference on Indoor Positioning and Indoor Navigation(IPIN2019). 2019年9月

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

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

UBICOMP/ISWC'19 2019年9月

歩容に頑健なPDRのための深層学習を用いた歩行速度推定手法

吉田拓人, 野崎惇登, 廣井慧, 梶克彦, 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） 2019年8月

ニューラルネットワークを用いた大規模イベント向けBLE屋内位置推定の比較評価

浦野健太，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シンポジウム2019論文集(DICOMO2019) 2019年7月

回転磁石マーカを用いたスマートフォン所持者の2次元経路推定

渡邊康祐, 廣井慧, 米澤拓郎, 梶克彦, 河口信夫

マルチメディア、分散協調とモバイルシンポジウム2019論文集(DICOMO2019) 2019年7月

人の回避行動を考慮した移動ロボットの経路計画法の検討

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シンポジウム2019論文集(DICOMO2019) 2019年7月

異なるサービス提供者間におけるトラストモデルとその実装に関する研究

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シンポジウム2019論文集(DICOMO2019) 2019年7月

自動運転車のレーザセンサを利用した路面性状調査査読有り

浦野健太, 廣井慧, 加藤真平, 駒形望, 河口信夫

マルチメディア、分散協調とモバイルシンポジウム2018論文集(DICOMO2018) - 巻 ( - ) 頁： 264 - 269 2019年7月

高齢者の外出促進を目的とした行動認識の適応学習およびチャットアプリケーションの実証実験査読有り

玉森聡, 石黒祥生, 廣井慧, 河口信夫, 武田一哉

コンシューマ・デバイス＆システム(CDS)研究会トランザクション 9 巻 ( 2 ) 頁： 33 - 46 2019年5月

Hybrid activity recognition for ballroom dance exercise using video and wearable sensor

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

2019 Joint 8th International Conference on Informatics, Electronics and Vision, ICIEV 2019 and 3rd International Conference on Imaging, Vision and Pattern Recognition, icIVPR 2019 with International Conference on Activity and Behavior Computing, ABC 2019 頁： 112 - 117 2019年5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：2019 Joint 8th International Conference on Informatics, Electronics and Vision, ICIEV 2019 and 3rd International Conference on Imaging, Vision and Pattern Recognition, icIVPR 2019 with International Conference on Activity and Behavior Computing, ABC 2019

In this paper, we propose a hybrid activity recognition method for ballroom dance exercise using video and wearable sensor. The purpose of our research is to design a mechanism to support ballroom dance exercise, and this paper reports the first part to design a mechanism is to support ballroom dance exercise, and this paper reports the first outcome to achieve the purpose - recognizing ballroom dance exercise. There are two conceivable ways to recognize dance exercise: videos and wearable sensor. However, each of them has its disadvantages. Using video is a good way to recognize the movement of the body. However, it cannot provide us accurate timing or strength of foot actions because the number of their flames per seconds is too small to recognize the fast movements of dancers. On the other hand, while a wearable sensor is good at recognizing foot timing and strength, it is not good at recognizing the movement of the whole body. Therefore we propose a hybrid recognition method utilizing the merits of both video and wearable sensor. This paper focuses to recognize four different types of steps in Latin American, a kind of ballroom dance. For each step, we record wearable sensing data and videos. As a result, it is found that the accuracy of step recognition is improved by adding wearable sensing data to video data shot from two different angles.

DOI： 10.1109/ICIEV.2019.8858524

Scopus

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

PerVehicle 2019 - 巻 ( - ) 頁： 826 - 831 2019年3月

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.

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Flood Area Estimation using Personal Location Data - Case Study of Japan Floods in 2018-

Hiroi Kei, Yoshida Takahiro, Yamagata Yoshiki, Kawaguchi Nobuo

2019 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS (PERCOM WORKSHOPS) 頁： 285-291 2019年

Sampling Rate Dependency in Pedestrian Walking Speed Estimation Using DualCNN-LSTM

Yoshida Takuto, Nozaki Junto, Urano Kenta, 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： 862-868 2019年

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