Faculty Profiles - YONEZAWA Takuro

Papers - YONEZAWA Takuro

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

Urano K., Hiroi K., Yonezawa T., Kawaguchi N.

2019 12th International Conference on Mobile Computing and Ubiquitous Network, ICMU 2019 2019.11

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

Rehabilitation Support Robot for Self-Standing-Up Training of Hemiplegic Stroke Patients - Design of Counterbalance Mechanism Reviewed

Tokinaga T., Yonezawa T., Saito M., Morita Y., Tanabe H., Kiss B.

International Conference on Control, Automation and Systems Vol. 2019-October page： 385 - 388 2019.10

Language：Japanese Publishing type：Research paper (scientific journal) Publisher：International Conference on Control, Automation and Systems

The objective of the study involved developing a rehabilitation support robot for self-standing-up training of hemiplegic stroke patients to restore normal standing-up motion. Based on our previous analysis of the therapist's guiding movements in standing-up training of patients, a passive-type robot was adopted. However, the link weight of the robot acts as a load at the patient's waist. In this work, we designed a counterbalance mechanism for the robot in order to reduce the load at the patient's waist and determined the optimal parameters for the mechanism. We confirmed by simulation that the specifications of the forces acting on the end-effectors of the robots were satisfied, and thus the proposed counterbalance mechanism was effective.

DOI： 10.23919/ICCAS47443.2019.8971502

Scopus

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 Reviewed

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(DICOMO2019)シンポジウム page： 22 - 28 2019.7

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(DICOMO2019)シンポジウム page： 840 - 845 2019.7

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 Reviewed

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(DICOMO2019)シンポジウム page： 284 - 290 2019.7

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 Reviewed

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(DICOMO2019)シンポジウム page： 29 - 35 2019.7

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

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

International Conference on Activity and Behavior Computing page： Paper66 2019.6

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

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 page： 112 - 117 2019.5

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

Cruisers: An automotive sensing platform for smart cities using door-to-door garbage collecting trucks. Reviewed

Array,Jin Nakazawa, Takuro Yonezawa, Hideyuki Tokuda

Ad Hoc Networks Vol. 85 page： 32 - 45 2019.3

Sampling rate dependency in pedestrian walking speed estimation using DualCNN-LSTM. Reviewed

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

Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, UbiComp/ISWC 2019 Adjunct, London, UK, September 9-13, 2019. page： 862 - 868 2019

Ballroom dance step type recognition by random forest using video and wearable sensor. Reviewed

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

CityFlow: exploiting edge computing for large scale smart city applications Reviewed

Giang Nam Ky, Leung Victor C. M., Kawano Makoto, Yonezawa Takuro, Nakazawa Jin, Lea Rodger, Broadbent Matt

2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP) page： 631-634 2019

Gait Dependency of Smartphone Walking Speed Estimation using Deep Learning. Reviewed

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. page： 641 - 642 2019

Basic Study of BLE Indoor Localization using LSTM-based Neural Network. Reviewed

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. page： 558 - 559 2019

Capturing Subjective Time as Context and It's Applications. Reviewed

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. page： 647 - 648 2019

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

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) page： 112 - 117 2019

CityFlow: Exploiting Edge Computing for Large Scale Smart City Applications. Reviewed

Nam Ky Giang, Victor C. M. Leung, Makoto Kawano, Takuro Yonezawa, Jin Nakazawa, Rodger Lea, Matt Broadbent

IEEE International Conference on Big Data and Smart Computing(BigComp) page： 1 - 4 2019

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

Urano Kenta, Hiroi Kei, Yonezawa Takuro, Kawaguchi Nobuo

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

Cruisers: An automotive sensing platform for smart cities using door-to-door garbage collecting trucks Reviewed

中澤仁, 米澤拓郎

International Journal of Ad Hoc Networks Vol. 85 page： 32 - 45 2019

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

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

CEUR Workshop Proceedings Vol. 2498 page： 463 - 470 2019

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

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Papers - YONEZAWA Takuro