研究者詳細 - 米澤　拓郎

論文 - 米澤　拓郎

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

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

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

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

International Conference on Control, Automation and Systems 2019-October 巻頁： 385 - 388 2019年10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：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次元経路推定査読有り

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)シンポジウム頁： 22 - 28 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(DICOMO2019)シンポジウム頁： 840 - 845 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査読有り

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)シンポジウム頁： 284 - 290 2019年7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査読有り

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)シンポジウム頁： 29 - 35 2019年7月

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

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

International Conference on Activity and Behavior Computing 頁： Paper66 2019年6月

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

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

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

Chen, Y; Nakazawa, J; Yonezawa, T; Tokuda, H

AD HOC NETWORKS 85 巻頁： 32 - 45 2019年3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：Ad Hoc Networks

Due to the advance in information and communication technologies, smart cities have become an increasingly appealing vision. One indispensable basis in the research, development and implementation of smart cities is urban sensing technology. Automotive sensing is a novel urban sensing technology that equips sensors to vehicles, like buses, taxi cabs or garbage trucks and utilizes their mobility to help to conduct urban sensing tasks. In this paper, we present Cruisers, an automotive sensing platform for smart cities, based on the following ideas: a) Garbage collecting trucks are used to host sensors, b) Cellular communication network is used to deliver sensed data to servers, and c) Proxy server(s) are adopted to convert the format of sensed data to required ones, such as sampling rate, spatial interval or pub/sub protocol. To validate the feasibility of Cruisers, we implement a real system that consists of 66 customized sensing modulars integrating typical environmental and mobility sensors and deploy them into the garbage collecting trucks at Fujisawa, a medium-sized city in Japan. Performance evaluation is conducted to investigate the performance of Cruisers in terms of scalability, data publishing delay and spatial coverage.

DOI： 10.1016/j.adhoc.2018.10.018

Web of Science

Scopus

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

Yoshida, T; Nozaki, J; Urano, K; Hiroi, K; Kaji, K; Yonezawa, T; Kawaguchi, N

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

記述言語：日本語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：ACM

There are many inertial sensor based indoor localization methods for smartphone, for example, SINS and PDR. However, most of the MEMS sensors of smartphones are not precise enough for these methods. We proposed end-to-end walking speed estimation method using deep learning to perform robust walking speed estimation with a low-precision sensor. Currently, we use the input data with a fixed format of 200 samples at 100 Hz. However, the sampling rate and sequence length should be changed appropriately depending on the required accuracy and terminal performance. They are critical factors when using our method for a long time on a terminal because continuous processing of a large amount of data leads to shorter battery life. In this paper, we evaluate the accuracy of the estimated speed by our method when changing the sampling rate and sequence length. As a result, using 5 patterns of combinations, the estimation accuracy hardly changed.

DOI： 10.1145/3341162.3343765

Web of Science

Scopus

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

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

記述言語：日本語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：ACM

The paper presents a hybrid ballroom dance step type recognition method using video and wearable sensors. Learning ballroom dance is very difficult for less experienced dancers as it has many complex types of steps. Therefore, our purpose is to recognize the various step types to support step learning. While the major approach to recognize dance performance is to utilize video, we cannot simply adopt it for ballroom dance because the dancers’ images overlap each other. To solve the problem, we propose a hybrid step recognition method combining video and wearable sensors for enhancing its accuracy and robustness. We collect seven dancers’ video and wearable sensors data including acceleration, angular velocity, and body parts location change. After that, we pre-process them and extract some feature values to recognize the step types. By adopting Random Forest for recognition, we confirmed that our approach achieved f1-score 0.760 for 13 step types recognition. Finally, we will open our dataset of ballroom dance to HASCA community for further research opportunities.

DOI： 10.1145/3341162.3344852

Web of Science

Scopus

CityFlow: exploiting edge computing for large scale smart city applications 査読有り

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) 頁： 631-634 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（国際会議プロシーディングス）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

Scopus

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（国際会議プロシーディングス）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

Scopus

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（国際会議プロシーディングス）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

Scopus

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

Urano Kenta, Hiroi Kei, Yonezawa Takuro, Kawaguchi Nobuo

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

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

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

CityFlow: Exploiting Edge Computing for Large Scale Smart City Applications 査読有り

Giang N.K., Leung V.C.M., Kawano M., Yonezawa T., Nakazawa J., Lea R., Broadbent M.

2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings 頁： 1 - 4 2019年

記述言語：日本語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings

This paper presents an approach to supporting the development process for large-scale smart city applications that leverage edge computing resources. A smart city testbed called CitiFlow is developed, which uses Distributed Node-RED as the underlying middleware to facilitate the decomposition and communication among sub components of large scale smart city applications. For the evaluation, a lab-based setup and a real world deployment were executed and are presented.

DOI： 10.1109/BIGCOMP.2019.8679234

Scopus

その他リンク： https://dblp.uni-trier.de/db/conf/bigcomp/bigcomp2019.html#GiangLKYNLB19

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

中澤仁, 米澤拓郎

International Journal of Ad Hoc Networks 85 巻頁： 32 - 45 2019年

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

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

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