Updated on 2024/03/01

写真a

 
FUJII Keisuke
 
Organization
Graduate School of Informatics Department of Intelligent Systems 1 Associate professor
Graduate School
Graduate School of Informatics
Undergraduate School
School of Informatics Department of Computer Science
School of Engineering Electrical Engineering, Electronics, and Information Engineering
Title
Associate professor
Contact information
メールアドレス
External link

Degree 3

  1. 博士(人間・環境学) ( 2014.3   京都大学 ) 

  2. 修士(人間・環境学) ( 2011.3   京都大学 ) 

  3. 学士(総合人間学) ( 2009.3   京都大学 ) 

Research Interests 3

  1. Collective motions

  2. Machine learning

  3. Sports Science

Research Areas 2

  1. Informatics / Intelligent informatics

  2. Life Science / Sports sciences

Current Research Project and SDGs 1

  1. Technologies for explanation and decision making in biological multi-agent motions

Research History 9

  1. Nagoya University   Graduate School of Informatics   Associate professor

    2021.5

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    Country:Japan

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  2. Japan Science and Technology Agency   PRESTO   Researcher

    2020.12

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  3. 理化学研究所   Advanced Intelligence Project   Visiting Scientist

    2019.5

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  4. Nagoya University   Graduate School of Informatics   Assistant Professor

    2019.4 - 2021.4

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    Country:Japan

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  5. 理化学研究所   革新知能統合研究センター   研究員

    2017.2 - 2019.4

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    Country:Japan

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  6. Japan Society for Promotion of Science

    2014.4 - 2017.1

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    Country:Japan

  7. Nagoya University

    2014.4 - 2017.1

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    Country:Japan

  8. Japan Society for Promotion of Science

    2011.4 - 2014.3

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    Country:Japan

  9. 京都大学大学院   人間・環境学研究科   博士課程

    2011.4 - 2014.3

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    Country:Japan

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Education 2

  1. Kyoto University   Graduate School, Division of Human and Environmental Studies

    2009.4 - 2014.3

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    Country: Japan

  2. Kyoto University   Faculty of Integrated Human Studies

    2005.4 - 2009.3

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    Country: Japan

Professional Memberships 5

  1. JAPANESE SOCIETY OF BIOMECHANICS

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  2. 日本バスケットボール学会

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  3. JAPAN SOCIETY OF PHYSICAL EDUCATION, HEALTH AND SPORT SCIENCES

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  4. THE SOCIETY OF INSTRUMENT AND CONTROL ENGINEERS

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  5. THE JAPANESE SOCIETY FOR ARTIFICIAL INTELLIGENCE

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Committee Memberships 17

  1. Neural Information Processing Systems (NeurIPS)   Program Committee  

    2022.12   

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    Committee type:Academic society

  2. British Machine Vision Conference (BMVC)   Program Committee  

    2022.11   

  3. International Conference on Learning Representations (ICLR)   Program Committee  

    2022.4   

  4. Conference on Neural Information Processing Systems (NeurIPS)   Program Committee  

    2021.12   

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    Committee type:Academic society

  5. International Joint Conferences on Artificial Intelligence (IJCAI)   Program Committee  

    2021.8   

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    Committee type:Academic society

  6. International Conference on Learning Representations (ICLR) 2021   Program Committee  

    2021.5   

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    Committee type:Academic society

  7. JSAI2020, 2021   Award committee  

    2020.6 - 2021.6   

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    Committee type:Academic society

  8. JSAI2020, 2021   Award committee  

    2020.6 - 2021.6   

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    Committee type:Academic society

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  9. Conference on Neural Information Processing Systems (NeurIPS)   Program Committee  

    2019.12 - 2020.12   

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    Committee type:Academic society

  10. Australasian AI conference 2019   Program Committee  

    2019.12   

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    Committee type:Academic society

  11. Workshop on Informatics: WinF 2019-2020   Program Committee  

    2019.11 - 2020.11   

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    Committee type:Academic society

  12. IBIS2019   Program Committee  

    2019.11   

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    Committee type:Academic society

  13. The Japan Society for Basketball Studies   Editorial committee  

    2019.4 - 2021.3   

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    Committee type:Academic society

  14. The Japan Society for Basketball Studies   Editorial committee  

    2019.4 - 2021.3   

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  15. AAAI Conference on Artificial Intelligence 2019, 2020   Program Committee  

    2019.2 - 2020.2   

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    Committee type:Academic society

  16. Kei-Hiroba (in Japanese Society for Biomechanics) 2020-2021   Organizing Committee  

       

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    Committee type:Academic society

  17. Kei-Hiroba (in Japanese Society for Biomechanics) 2020-2021   Organizing Committee  

       

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Awards 35

  1. Akasaki Prize

    2023.3   Nagoya University  

    Keisuke Fujii

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    Country:Japan

  2. Best Poster Award

    2022.11   ACM SIGSPATIAL 2022   , Estimating counterfactual treatment outcomes over time in complex multi-vehicle simulation

    Keisuke Fujii, Koh Takeuchi, Atsushi Kuribayashi, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda

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    Award type:Award from international society, conference, symposium, etc.  Country:United States

  3. Best Poster Award

    2022.11   ACM SIGSPATIAL 2022   , Estimating counterfactual treatment outcomes over time in complex multi-vehicle simulation

    Keisuke Fujii, Koh Takeuchi, Atsushi Kuribayashi, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda

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    Award type:Award from international society, conference, symposium, etc.  Country:United States

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  4. Excellence Award (Soccer Division)

    2024.1   The 13th Sports Data Analysis Competition (Statistics in Sports: A section in Japan Statistical Society)  

    Aru Ota, Keisuke Fujii, Hirohisa Hioki

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  5. Excellence Award (Soccer Division)

    2024.1   The 13th Sports Data Analysis Competition (Statistics in Sports: A section in Japan Statistical Society)  

    Taiga Someya, Kohei Kawaguchi, Keisuke Fujii

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  6. SSI Best Presentation Award

    2023.11   The Society of Instrument and Control Engineers (SICE)  

    Shoko Genda, Yu Teshima, Daichi Ohara, Yotai Aoki, Keisuke Fujii, Shizuko Hiryu

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  7. JSAI Annual Conference Award

    2023.5   The Japanese Society for Artificial Intelligence Annual Conference (JSAI'23)  

    Kazushi Tsutsui, Ryoya Tanaka, Kazuya Takeda, Keisuke Fujii

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  8. Best Paper Award

    2023.5   The First IEEE International Conference on Mobility: Operations, Services, and Technologies (MOST'23)  

    Robin Karlsson, Alexander Carballo, Keisuke Fujii, Kento Ohtani, Kazuya Takeda

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  9. A Prize (3rd place), Soccer Division

    2023.1   The 12th Sports Data Analysis Competition (Statistics in Sports: A section in Japan Statistical Society)  

    Rikuhei Umemoto, Hiroshi Nakahara, Kazushi Tsutsui, Keisuke Fujii

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    Award type:Award from Japanese society, conference, symposium, etc. 

  10. IEEE GCCE 2022 Excellent Student Paper Award, Gold Prize

    2022.11   IEEE 11th Global Conference on Consumer Electronics (GCCE 2022)   Detection in Race Walking From a Smartphone Camera via Fine-Tuning Pose Estimation

    Tomohiro Suzuki, Kazuya Takeda, Keisuke Fujii

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    Award type:Award from international society, conference, symposium, etc. 

  11. Presentation Award (Student)

    2022.6   4th Kei-Hiroba (Biomechanics Research Society)  

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    Award type:Award from Japanese society, conference, symposium, etc. 

  12. Best Presentation Award Finalist

    2022.3   24th Information-Based Induction Science and Machine Learning (IBIS2021)   Learning interaction rules from multi-animal trajectories via augmented behavioral models,

    Keisuke Fujii, Naoya Takeishi, Kazushi Tsutsui, Emyo Fujioka, Nozomi Nishiumi, Ryoya Tanaka, Mika Fukushiro, Kaoru Ide, Hiroyoshi Kohno, Ken Yoda, Susumu Takahashi, Shizuko Hiryu, Yoshinobu Kawahara

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    Award type:Award from Japanese society, conference, symposium, etc.  Country:Japan

  13. Excellence Award (Soccer Division)

    2022.1   The 11th Sports Data Analysis Competition (Statistics in Sports: A section in Japan Statistical Society)  

    Masakiyo Teranishi, Kazushi Tsutsui, Kazuya Takeda, Keisuke Fujii

  14. A Prize (3rd place), Baseball Division

    2022.1   The 11th Sports Data Analysis Competition (Statistics in Sports: A section in Japan Statistical Society)  

    Hiroshi Nakahara, Keisuke Fujii

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    Award type:Award from Japanese society, conference, symposium, etc. 

  15. Spoana award

    2021.8   ARCS IDEATHON (Sports Analyst Meetup #11 and NTT Communications rugby football club)  

    Rory Bunker, Keisuke Fujii

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    Award type:Award from Japanese society, conference, symposium, etc. 

  16. Presentation Award (Student)

    2021.6   3rd Kei-Hiroba (Biomechanics Research Society)  

    Tatsuya Yoshikawa, Keisuke Fujii, Kazuya Takeda

  17. サッカー部門 優秀賞

    2021.1   第10回日本統計学会スポーツ統計分科会スポーツデータ解析コンペティション   サッカーにおけるボール奪取・被有効攻撃予測に基づくチームの守備評価

    戸田康介・寺西真聖・久代恵介・藤井慶輔

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    Award type:Award from Japanese society, conference, symposium, etc.  Country:Japan

  18. Excellence Award (Soccer Division)

    2021.1   The 10th Sports Data Analysis Competition (Statistics in Sports: A section in Japan Statistical Society)   Evaluation of soccer team defense based on prediction models of ball recovery and being attacked

    Kosuke Toda, Masakiyo Teranishi, Keisuke Kushiro, Keisuke Fujii

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    Award type:Award from Japanese society, conference, symposium, etc.  Country:Japan

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  19. 口頭発表賞

    2020.12   日本バスケットボール学会 第7回大会  

    藤井 慶輔, 武石 直也, 河原 吉伸, 武田 一哉

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  20. Best Paper Award in 2020

    2020.12   Japanese Neural Network Society   Dynamic mode decomposition in vector-valued reproducing kernel Hilbert spaces for extracting dynamical structure among observables

    Keisuke Fujii, Yoshinobu Kawahara

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  21. 大会発表賞

    2020.12   日本認知科学会第37回大会   なぞり課題を用いた運動協調における役割の検討

    市川淳・藤井慶輔

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    Award type:Award from Japanese society, conference, symposium, etc.  Country:Japan

  22. Presentation Award

    2020.12   37th Annual Meeting of the Japanese Cognitive Science Society   Investigation of Roles in Group Behavior Using a Coordinative Drawing Task

    Jun Ichikawa, Keisuke Fujii

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    Award type:Award from Japanese society, conference, symposium, etc.  Country:Japan

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  23. Excellent Student Paper Award (On-demand), Bronze Prize

    2020.11   IEEE 9th Global Conference on Consumer Electronics (GCCE 2020)   Trajectory prediction with imitation learning reflecting defensive evaluation in team sports

    Masakiyo Teranishi, Keisuke Fujii, Kazuya Takeda

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    Award type:Award from international society, conference, symposium, etc.  Country:Japan

  24. Excellent Student Paper Award (On-demand), Bronze Prize

    2020.11   IEEE 9th Global Conference on Consumer Electronics (GCCE 2020)   Trajectory prediction with imitation learning reflecting defensive evaluation in team sports

    Masakiyo Teranishi, Keisuke Fujii, Kazuya Takeda

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    Award type:Award from international society, conference, symposium, etc.  Country:Japan

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  25. オープン部門 発表賞

    2020.6   第2回彗ひろば(バイオメカニクス研究会)  

    藤井 慶輔

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  26. 学生部門 発表賞

    2020.6   第2回彗ひろば(バイオメカニクス研究会)   サッカーにおける守備評価を考慮したデータ駆動的集団運動モデリング

    寺西真聖, 藤井慶輔, 武田一哉

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    Award type:Award from Japanese society, conference, symposium, etc.  Country:Japan

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  27. 学生部門 発表賞

    2020.6   第2回彗ひろば(バイオメカニクス研究会)   サッカーにおける守備評価を考慮したデータ駆動的集団運動モデリング

    寺西真聖, 藤井慶輔, 武田一哉

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    Award type:Award from Japanese society, conference, symposium, etc.  Country:Japan

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  28. 口頭発表賞

    2019.12   日本バスケットボール学会 第6回大会  

    藤井 慶輔

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  29. Oral Presentation Award

    2019.12   The Japan Society for Basketball Studies   Automatic classification of team strategy using machine learning

    Keisuke Fujii

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    Award type:Award from Japanese society, conference, symposium, etc.  Country:Japan

  30. Presentation Award

    2019.6   1st Kei-Hiroba (Biomechanics Research Society)   Data-driven spectral analysis for social biomechanics

    Keisuke Fujii, Naoya Takeishi, Yuki Inaba, Benio Kibushi, Motoki Kouzaki, Yoshinobu Kawahara

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  31. 桜舞賞(研究奨励賞)

    2019.3   理化学研究所  

    藤井 慶輔

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  32. べストプレゼンテーション賞

    2018.11   第21回情報論的学習理論ワークショップ   観測量間の動的構造を抽出するベクトル値再生核ヒルベルト空間における動的モード分解

    藤井 慶輔・河原 吉伸

  33. 若手研究優秀賞

    2015.8   第66回日本体育学会   球技における味方を助ける行動―空間マルチスケール性に着目して

    藤井 慶輔・小山 孟志・陸川 章・山田 洋・山本 裕二

  34. 若手研究奨励賞

    2011.3   第140回京都体育学会大会   球技における防御者が意思決定に用いる情報とは何か―倒立振子モデルによるアプローチ―

    藤井 慶輔・進矢正宏・山下大地・小田伸午

  35. Best Paper Award

    The 21st Workshop on Informatics 2022 (WiNF 2023)  

    Ning Ding, Kazuya Takeda, Wenhui Jin, Yingjiu Bei, Keisuke Fujii

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Papers 70

  1. Estimating counterfactual treatment outcomes over time in complex multi-vehicle simulation Reviewed

    Keisuke Fujii, Koh Takeuchi, Atsushi Kuribayashi, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda

    30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2022)     2022.11

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  2. Learning interaction rules from multi-animal trajectories via augmented behavioral model Reviewed

    Keisuke Fujii, Naoya Takeishi, Kazushi Tsutsui, Emyo Fujioka, Nozomi Nishiumi, Ryoya Tanaka, Mika Fukushiro, Kaoru Ide, Hiroyoshi Kohno, Ken Yoda, Susumu Takahashi, Shizuko Hiryu, Yoshinobu Kawahara

    Advances in Neural Information Processing Systems (NeurIPS'21)   Vol. 34   2021.12

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  3. Data-Driven Analysis for Understanding Team Sports Behaviors

    Keisuke Fujii

    Journal of Robotics and Mechatronics   Vol. 33 ( 3 ) page: 505 - 514   2021.6

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    Authorship:Lead author, Last author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Fuji Technology Press Ltd.  

    <p>Understanding the principles of real-world biological multi-agent behaviors is a current challenge in various scientific and engineering fields. The rules regarding the real-world biological multi-agent behaviors such as those in team sports are often largely unknown due to their inherently higher-order interactions, cognition, and body dynamics. Estimation of the rules from data, i.e., via data-driven approaches such as machine learning, provides an effective way to analyze such behaviors. Although most data-driven models have non-linear structures and high predictive performances, it is sometimes hard to interpret them. This survey focuses on data-driven analysis for quantitative understanding of behaviors in invasion team sports such as basketball and football, and introduces two main approaches for understanding such multi-agent behaviors: (1) extracting easily interpretable features or rules from data and (2) generating and controlling behaviors in visually-understandable ways. The first approach involves the visualization of learned representations and the extraction of mathematical structures behind the behaviors. The second approach can be used to test hypotheses by simulating and controlling future and counterfactual behaviors. Lastly, the potential practical applications of extracted rules, features, and generated behaviors are discussed. These approaches can contribute to a better understanding of multi-agent behaviors in the real world.</p>

    DOI: 10.20965/jrm.2021.p0505

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  4. Physically-interpretable classification of biological network dynamics for complex collective motions. Reviewed International journal

    Keisuke Fujii, Naoya Takeishi, Motokazu Hojo, Yuki Inaba, Yoshinobu Kawahara

    Scientific Reports   Vol. 10 ( 1 ) page: 3005 - 3005   2020.2

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    Understanding biological network dynamics is a fundamental issue in various scientific and engineering fields. Network theory is capable of revealing the relationship between elements and their propagation; however, for complex collective motions, the network properties often transiently and complexly change. A fundamental question addressed here pertains to the classification of collective motion network based on physically-interpretable dynamical properties. Here we apply a data-driven spectral analysis called graph dynamic mode decomposition, which obtains the dynamical properties for collective motion classification. Using a ballgame as an example, we classified the strategic collective motions in different global behaviours and discovered that, in addition to the physical properties, the contextual node information was critical for classification. Furthermore, we discovered the label-specific stronger spectra in the relationship among the nearest agents, providing physical and semantic interpretations. Our approach contributes to the understanding of principles of biological complex network dynamics from the perspective of nonlinear dynamical systems.

    DOI: 10.1038/s41598-020-58064-w

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  5. Data-driven spectral analysis for coordinative structures in periodic human locomotion. Reviewed International journal

    Keisuke Fujii, Naoya Takeishi, Benio Kibushi, Motoki Kouzaki, Yoshinobu Kawahara

    Scientific reports   Vol. 9 ( 1 ) page: 16755 - 16755   2019.11

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    Living organisms dynamically and flexibly operate a great number of components. As one of such redundant control mechanisms, low-dimensional coordinative structures among multiple components have been investigated. However, structures extracted from the conventional statistical dimensionality reduction methods do not reflect dynamical properties in principle. Here we regard coordinative structures in biological periodic systems with unknown and redundant dynamics as a nonlinear limit-cycle oscillation, and apply a data-driven operator-theoretic spectral analysis, which obtains dynamical properties of coordinative structures such as frequency and phase from the estimated eigenvalues and eigenfunctions of a composition operator. Using segmental angle series during human walking as an example, we first extracted the coordinative structures based on dynamics; e.g. the speed-independent coordinative structures in the harmonics of gait frequency. Second, we discovered the speed-dependent time-evolving behaviours of the phase by estimating the eigenfunctions via our approach on the conventional low-dimensional structures. We also verified our approach using the double pendulum and walking model simulation data. Our results of locomotion analysis suggest that our approach can be useful to analyse biological periodic phenomena from the perspective of nonlinear dynamical systems.

    DOI: 10.1038/s41598-019-53187-1

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  6. Dynamic mode decomposition in vector-valued reproducing kernel Hilbert spaces for extracting dynamical structure among observables. Reviewed International journal

    Keisuke Fujii, Yoshinobu Kawahara

    Neural networks : the official journal of the International Neural Network Society   Vol. 117   page: 94 - 103   2019.9

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    Understanding nonlinear dynamical systems (NLDSs) is challenging in a variety of engineering and scientific fields. Dynamic mode decomposition (DMD), which is a numerical algorithm for the spectral analysis of Koopman operators, has been attracting attention as a way of obtaining global modal descriptions of NLDSs without requiring explicit prior knowledge. However, since existing DMD algorithms are in principle formulated based on the concatenation of scalar observables, it is not directly applicable to data with dependent structures among observables, which take, for example, the form of a sequence of graphs. In this paper, we formulate Koopman spectral analysis for NLDSs with structures among observables and propose an estimation algorithm for this problem. This method can extract and visualize the underlying low-dimensional global dynamics of NLDSs with structures among observables from data, which can be useful in understanding the underlying dynamics of such NLDSs. To this end, we first formulate the problem of estimating spectra of the Koopman operator defined in vector-valued reproducing kernel Hilbert spaces, and then develop an estimation procedure for this problem by reformulating tensor-based DMD. As a special case of our method, we propose the method named as Graph DMD, which is a numerical algorithm for Koopman spectral analysis of graph dynamical systems, using a sequence of adjacency matrices. We investigate the empirical performance of our method by using synthetic and real-world data.

    DOI: 10.1016/j.neunet.2019.04.020

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  7. Automatic detection of faults in race walking from a smartphone camera: a comparison of an Olympic medalist and university athletes Reviewed

    Tomohiro Suzuki, Kazuya Takeda, Keisuke Fujii

    International Journal of Computer Science in Sport     2024.3

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  8. Decentralized policy learning with partial observation and mechanical constraints for multiperson modeling Reviewed

    Keisuke Fujii, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda

    Neural Networks   Vol. 171   page: 40 - 52   2024.3

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    DOI: 10.1016/j.neunet.2023.11.068

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  9. Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations Reviewed

    Keisuke Fujii, Kazushi Tsutsui, Atom Sco, Hiroshi Nakahara, Naoya Takeishi, Yoshinobu Kawahara

    International Conference on Agents and Artificial Intelligence (ICAART 2024)   Vol. 2   page: 27 - 39   2024.2

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  10. Estimating counterfactual treatment outcomes over time in complex multi-agent scenarios Reviewed

    Keisuke Fujii, Koh Takeuchi, Atsushi Kuribayashi, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda

    IEEE Transactions on Neural Networks and Learning Systems     2024.2

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  11. Extracting proficiency differences and individual characteristics in golfers' swing using single-video markerless motion analysis Reviewed

    Kota Yamamoto, Yumiko Hasegawa, Tomohiro Suzuki, Hiroo Suzuki, Hiroko Tanabe, Keisuke Fujii

    Frontiers in Sports and Active Living   Vol. 5   2023.11

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Frontiers Media SA  

    In this study, we analyzed golfers' swing movement to extract differences in proficiency and individual characteristics using two-dimensional video data from a single camera. We conducted an experiment with 27 golfers who had a wide range of skill levels, using a 7-iron; we acquired video data with a camera on the sagittal plane. For data extraction, we used pose estimation (using HRNet) and object detection (using DeepLabCut) methods to extract human-joint and club-head data. We examined the relationship between proficiency and individual characteristics vis-à-vis forward tilt angle and club trajectory. The results showed that the stability and reproducibility of the forward tilt angle are characteristics of proficiency. Highly skilled golfers showed low variability and high reproducibility between trials in forward tilt angle. However, we found that club trajectory may not be a characteristic of proficiency but rather an individual characteristic. Club trajectory was divided roughly into clockwise rotation and counterclockwise rotation. Thus, the analysis based on video data from a single markerless camera enabled the extraction of the differences in proficiency and individual characteristics of golf swing. This suggests the usefulness of our system for simply evaluating golf swings and applying it to motor learning and coaching situations.

    DOI: 10.3389/fspor.2023.1272038

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  12. Can golfers choose low-risk routes in steep putting based on visual feedback of ball trajectory? Reviewed International journal

    Yumiko Hasegawa, Ayako Okada, Keisuke Fujii

    Frontiers in Sports and Active Living   Vol. 5   page: 1131390 - 1131390   2023.8

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    This study aims to clarify why the aiming method in golf putting in risky situations differs based on skill level. This study set up a difficult challenge (steep slopes and fast ball rolling greens), which required even professional golfers to change their aim. A total of 12 tour professionals and 12 intermediate amateurs were asked to perform a steep-slope task with no visual feedback of outcomes (no FB) followed by a task with visual feedback (with FB). The aim of the task was for the ball to enter the hole in one shot. Additionally, the participants were told that if the ball did not enter the hole, it was to at least stop as close to it as possible. The participant's aim (as an angle) and the kinematics of the putter head and ball were measured. The results indicated that professionals' highest ball trajectory points were significantly higher than that of amateurs, especially with FB. Additionally, professionals had higher ball-launch angles (the direction of the ball when the line connecting the ball and the center of the hole is 0 degrees) and lower peak putter head velocities than amateurs. Furthermore, the aim angle, indicating the golfer's decision-making, was higher for professionals under both conditions. However, even with FB, the amateurs' aim angles were lower and the difference between trials was smaller than that of professionals. Therefore, this study confirmed that the professionals made more drastic changes to their aim to find low-risk routes than the amateurs and that the amateurs’ ability to adjust their aim was lower than that of professionals. The results suggest that the reason for the amateurs' inability to find low-risk routes lies in their decision-making. The professionals found better routes; however, there were individual differences in their routes.

    DOI: 10.3389/fspor.2023.1131390

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  13. Estimation of control area in badminton doubles with pose information from top and back view drone videos Reviewed

    Ning Ding, Kazuya Takeda, Wenhui Jin, Yingjiu Bei, Keisuke Fujii

    Multimedia Tools and Applications     2023.8

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    Abstract

    The application of visual tracking to the performance analysis of sports players in dynamic competitions is vital for effective coaching. In doubles matches, coordinated positioning is crucial for maintaining control of the court and minimizing opponents’ scoring opportunities. The analysis of such teamwork plays a vital role in understanding the dynamics of the game. However, previous studies have primarily focused on analyzing and assessing singles players without considering occlusion in broadcast videos. These studies have relied on discrete representations, which involve the analysis and representation of specific actions (e.g., strokes) or events that occur during the game while overlooking the meaningful spatial distribution. In this work, we present the first annotated drone dataset from top and back views in badminton doubles and propose a framework to estimate the control area probability map, which can be used to evaluate teamwork performance. We present an efficient framework of deep neural networks that enables the calculation of full probability surfaces. This framework utilizes the embedding of a Gaussian mixture map of players’ positions and employs graph convolution on their poses. In the experiment, we verify our approach by comparing various baselines and discovering the correlations between the score and control area. Additionally, we propose a practical application for assessing optimal positioning to provide instructions during a game. Our approach offers both visual and quantitative evaluations of players’ movements, thereby providing valuable insights into doubles teamwork. The dataset and related project code is available at https://github.com/Ning-D/Drone_BD_ControlArea

    DOI: 10.1007/s11042-023-16362-1

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  14. Synergizing Deep Reinforcement Learning and Biological Pursuit Behavioral Rule for Robust and Interpretable Navigation Reviewed

    Kazushi Tsutsui, Kazuya Takeda, Keisuke Fujii

    1st Workshop on the Synergy of Scientific and Machine Learning Modeling (SynS and ML) co-located with the International Conference on Machine Learning (ICML'23)     2023.8

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  15. Pitching strategy evaluation via stratified analysis using propensity score Reviewed

    Hiroshi Nakahara, Kazuya Takeda, Keisuke Fujii

    Journal of Quantitative Analysis in Sports   Vol. 19 ( 2 ) page: 91 - 102   2023.5

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    Abstract

    Recent measurement technologies enable us to analyze baseball at higher levels of complexity. There are, however, still many unclear points around pitching strategy. There are two elements that make it difficult to measure the effect of a pitching strategy. First, most public datasets do not include location data where the catcher demands a ball, which is essential information to obtain the battery’s intent. Second, there are many confounders associated with pitching/batting results when evaluating pitching strategy. We here clarify the effect of pitching attempts to a specific location, e.g., inside or outside. We employ a causal inference framework called stratified analysis using a propensity score to evaluate the effects while removing the effect of confounding factors. We use a pitch-by-pitch dataset of Japanese professional baseball games held in 2014–2019, which includes location data where the catcher demands a ball. The results reveal that an outside pitching attempt is more effective than an inside one to minimize allowed run average. In addition, the stratified analysis shows that the outside pitching attempt is effective regardless of the magnitude of the estimated batter’s ability, and the proportion of pitched inside for pitcher/batter. Our analysis provides practical insights into selecting a pitching strategy to minimize allowed runs.

    DOI: 10.1515/jqas-2021-0060

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    Other Link: https://www.degruyter.com/document/doi/10.1515/jqas-2021-0060/pdf

  16. Prediction of Bat Flight Path During Obstacle Avoidance by Imitation Learning Reviewed

    Shoko Genda, Yu Teshima, Taku Kawamura, Daichi Ohara, Keisuke Fujii, Shizuko Hiryu

    2nd International Workshop on Behavior analysis and Recognition for knowledge Discovery (BiRD 2023) in conjunction with the IEEE International Conference on Pervasive Computing and Communications (PerCom 2023)     2023.3

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  17. Biomechanical strategies to maximize gait attractiveness among women Reviewed

    Hiroko Tanabe, Keisuke Fujii, Naotsugu Kaneko, Hikaru Yokoyama, Kimitaka Nakazawa

    Frontiers in Sports Active Living   Vol. 5 ( 1091470 )   2023.2

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  18. Estimating the Effect of Team Hitting Strategies Using Counterfactual Virtual Simulation in Baseball, Reviewed

    Hiroshi Nakahara, Kazuya Takeda, Keisuke Fujii,

    International Journal of Computer Science in Sport   Vol. 22 ( 1 ) page: 1 - 12   2023.1

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  19. Predictive World Models from Real-World Partial Observations. Reviewed

    Robin Karlsson, Alexander Carballo, Keisuke Fujii 0001, Kento Ohtani, Kazuya Takeda

    MOST     page: 152 - 166   2023

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    DOI: 10.1109/MOST57249.2023.00024

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    Other Link: https://dblp.uni-trier.de/db/conf/most/most2023.html#KarlssonC0OT23

  20. Multi-Agent Deep-Learning Based Comparative Analysis of Team Sport Trajectories. Reviewed

    Zhang Ziyi, Rory P. Bunker, Kazuya Takeda, Keisuke Fujii 0001

    IEEE Access   Vol. 11   page: 43305 - 43315   2023

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    DOI: 10.1109/ACCESS.2023.3269287

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  21. Learning to Predict Navigational Patterns from Partial Observations Reviewed

    Robin Karlsson, Alexander Carballo, Francisco Lepe-Salazar, Keisuke Fujii, Kento Ohtani, Kazuya Takeda

    IEEE Robotics and Automation Letters     2023

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    Human beings cooperatively navigate rule-constrained environments by adhering to mutually known navigational patterns, which may be represented as directional pathways or road lanes. Inferring these navigational patterns from incompletely observed environments is required for intelligent mobile robots operating in unmapped locations. However, algorithmically defining these navigational patterns is nontrivial. This paper presents the first self-supervised learning (SSL) method for learning to infer navigational patterns in real-world environments from partial observations only. We explain how geometric data augmentation, predictive world modeling, and an information-theoretic regularizer enable our model to predict an unbiased local directional soft lane probability (DSLP) field in the limit of infinite data. We demonstrate how to infer global navigational patterns by fitting a maximum likelihood graph to the DSLP field. Experiments show that our SSL model outperforms two SOTA supervised lane graph prediction models on the nuScenes dataset. We propose our SSL method as a scalable and interpretable continual learning paradigm for navigation by perception.

    DOI: 10.1109/LRA.2023.3291924

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  22. A framework of interpretable match results prediction in football with FIFA ratings and team formation. Reviewed International journal

    Calvin C K Yeung, Rory Bunker, Keisuke Fujii

    PloS one   Vol. 18 ( 4 ) page: e0284318   2023

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    While forecasting football match results has long been a popular topic, a practical model for football participants, such as coaches and players, has not been considered in great detail. In this study, we propose a generalized and interpretable machine learning model framework that only requires coaches' decisions and player quality features for forecasting. By further allowing the model to embed historical match statistics, features that consist of significant information, during the training process the model was practical and achieved both high performance and interpretability. Using five years of data (over 1,700 matches) from the English Premier League, our results show that our model was able to achieve high performance with an F1-score of 0.47, compared to the baseline betting odds prediction, which had an F1-score of 0.39. Moreover, our framework allows football teams to adapt for tactical decision-making, strength and weakness identification, formation and player selection, and transfer target validation. The framework in this study would have proven the feasibility of building a practical match result forecast framework and may serve to inspire future studies.

    DOI: 10.1371/journal.pone.0284318

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  23. Action Valuation of On- and Off-Ball Soccer Players Based on Multi-Agent Deep Reinforcement Learning. Reviewed

    Hiroshi Nakahara, Kazushi Tsutsui, Kazuya Takeda, Keisuke Fujii 0001

    IEEE Access   Vol. 11   page: 131237 - 131244   2023

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    DOI: 10.1109/ACCESS.2023.3336425

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  24. Score prediction using multiple object tracking for analyzing movements in 2-vs-2 Handball. Reviewed

    Ren Kobayashi, Rikuhei Umemoto, Kazuya Takeda, Keisuke Fujii 0001

    GCCE     page: 946 - 947   2023

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    DOI: 10.1109/GCCE59613.2023.10315410

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    Other Link: https://dblp.uni-trier.de/db/conf/gcce/gcce2023.html#KobayashiUTF23

  25. Automatic Edge Error Judgment in Figure Skating Using 3D Pose Estimation from Inertial Sensors. Reviewed

    Ryota Tanaka, Tomohiro Suzuki, Kazuya Takeda, Keisuke Fujii 0001

    GCCE     page: 1099 - 1100   2023

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    DOI: 10.1109/GCCE59613.2023.10315532

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  26. Automatic Edge Error Judgment in Figure Skating Using 3D Pose Estimation from a Monocular Camera and IMUs. Reviewed

    Ryota Tanaka, Tomohiro Suzuki, Kazuya Takeda, Keisuke Fujii 0001

    MMSports@MM     page: 41 - 48   2023

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    DOI: 10.1145/3606038.3616171

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    Other Link: https://dblp.uni-trier.de/db/conf/mmsports/mmsports2023.html#TanakaST023

  27. A sense of distance and movement characteristics of golfers tested without visual feedback of outcomes: Is a putt that feels subjectively good also physically good? Reviewed

    Yumiko Hasegawa, Ayako Okada, Keisuke Fujii

    Frontiers in Sports and Active Living   Vol. 4 ( 987493 )   2022.10

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  28. Automatic Fault Detection in Race Walking From a Smartphone Camera via Fine-Tuning Pose Estimation Reviewed

    Tomohiro Suzuki, Kazuya Takeda, Keisuke Fujii

    IEEE 11th Global Conference on Consumer Electronics (GCCE 2022)     2022.10

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  29. 集団運動における機械学習を用いたデータ科学 Reviewed

    藤井 慶輔

    人工知能学会研究会資料 人工知能基本問題研究会   Vol. 121 ( 0 ) page: 18 - 18   2022.9

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    DOI: 10.11517/jsaifpai.121.0_18

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  30. Optimization of aircraft flight paths considering the conflicting parameters of economy and safety Reviewed

    Tetsuya Nagashima, Ming Ding, Keisuke Fujii, Kazuya Takeda

    33rd Congress of the International Council of the Aeronautical Sciences     2022.9

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  31. Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction Reviewed

    Masakiyo Teranishi, Kazushi Tsutsui, Kazuya Takeda, Keisuke Fujii

    9th Workshop on Machine Learning and Data Mining for Sports Analytics 2022 (MLSA'22) co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD'22)     2022.9

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  32. Emergence of Collaborative Hunting via Multi-Agent Deep Reinforcement Learning, International Workshop on Human Behavior Understanding Reviewed

    Kazushi Tsutsui, Kazuya Takeda, Keisuke Fujii

        2022.8

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  33. SoccerTrack: A Dataset and Tracking Algorithm for Soccer with Fish-eye and Drone Videos. Reviewed

    Atom Scott, Ikuma Uchida, Masaki Onishi, Yoshinari Kameda, Kazuhiro Fukui, Keisuke Fujii 0001

    CVPR Workshops     page: 3568 - 3578   2022.6

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    DOI: 10.1109/CVPRW56347.2022.00401

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    Other Link: https://dblp.uni-trier.de/db/conf/cvpr/cvpr2022w.html#ScottUOKF022

  34. Deep Reinforcement Learning in a Racket Sport for Player Evaluation With Technical and Tactical Contexts. Reviewed

    Ning Ding, Kazuya Takeda, Keisuke Fujii

    IEEE Access   Vol. 10   page: 54764 - 54772   2022.5

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    DOI: 10.1109/ACCESS.2022.3175314

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  35. Discriminant Dynamic Mode Decomposition for Labeled Spatiotemporal Data Collections. Reviewed

    Naoya Takeishi, Keisuke Fujii, Koh Takeuchi, Yoshinobu Kawahara

    SIAM J. Appl. Dyn. Syst.   Vol. 21 ( 2 ) page: 1030 - 1058   2022.5

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    DOI: 10.1137/21m1399907

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  36. How does AI play football? An analysis of RL and real-world football strategies Reviewed

    Atom Scott, Keisuke Fujii, Masaki Onishi

    International Conference on Agents and Artificial Intelligence (ICAART 2022)   Vol. 1   page: 42 - 52   2022.2

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  37. Evaluation of soccer team defense based on prediction models of ball recovery and being attacked: A pilot study Reviewed

    Kosuke Toda, Masakiyo Teranishi, Keisuke Kushiro & Keisuke Fujii

    PLoS One   Vol. 17 ( 1 ) page: e0263051   2022.1

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  38. A basic study on the mechanism of group behavior of wild bats using movement pattern measurement and granger causality during nesting Reviewed

    USHIO Kazusa, FUJIOKA Emyo, FUJI Keisuke, HABE Hitoshi, KAWASHIMA Hiroaki, HIRYU Shizuko

    Proceedings of the Annual Conference of JSAI   Vol. JSAI2022 ( 0 ) page: 3G4OS15b02 - 3G4OS15b02   2022

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    <p>Bats recognize their surrounding environment by processing the echoes of ultrasonic waves emitted by themselves. Many species of bats live in groups, and many individuals emerge together from roosts. In this study, we used high-sensitivity video cameras to measure the flight trajectories of bats emerging from the cave in three dimensions, and investigated their flight trails. As a result, we found there were three behavioral patterns during emerging: exiting the cave, returning to the cave, and some other action. In addition, we applied the Granger causality method (Fujii et al., NeurlPS'21) to analyze the swarm behavior mechanism of emerging bats. The results showed that forward individuals flew in such a way that they were "repulsed" from or "approached" the other individuals. This suggests that bats, which use sound to understand their environment, are also influenced by backward individuals, which cannot be captured visually, suggesting that bats have a unique swarming mechanism that differs from model animals for group behavior, mainly visual animals.</p>

    DOI: 10.11517/pjsai.jsai2022.0_3g4os15b02

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  39. Evaluation of soccer players to create scoring opportunities for teammates based on their trajectory prediction Reviewed

    TERANISHI Masakiyo, TSUTSUI Kazushi, TAKEDA Kazuya, FUJII Keisuke

    Proceedings of the Annual Conference of JSAI   Vol. JSAI2022 ( 0 ) page: 3G4OS15b05 - 3G4OS15b05   2022

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    <p>Soccer is a game in which many players and the ball interact in complex ways. Regarding the quantitative evaluation of soccer attackers, there have been many and few studies on the player with and without the ball, respectively. However, it is still difficult to evaluate an attacking player without the ball and intention to receive it, and to reveal how movement contributes to the creation of scoring opportunities compared to typical (or predicted) movements. In this paper, we evaluate players who create off-ball scoring opportunities by comparing the reference movements generated by trajectory prediction with actual movements. In the proposed method, first, the trajectory is predicted using a graph variational recurrent neural network that can accurately model the relationship between players and predict the long-term trajectory. Next, based on the difference in the existing off-ball evaluation index between the actual data and the predicted trajectory, we evaluate how the actual movement contributes to scoring opportunity compared to the predicted movement as a reference. In the verification, we show that the evaluation of the proposed method is intuitive, using the relationship with the scores with all 18 teams in the Japanese professional soccer league and the example of one game.</p>

    DOI: 10.11517/pjsai.jsai2022.0_3g4os15b05

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  40. Estimating the Effect of Team Hitting Strategies Using Counterfactual Virtual Simulation in Baseball Reviewed

    NAKAHARA Hiroshi, TAKEDA Kazuya, FUJII Keisuke

    Proceedings of the Annual Conference of JSAI   Vol. JSAI2022 ( 0 ) page: 3G4OS15b04 - 3G4OS15b04   2022

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    <p>In baseball, every play on the field is quantitatively evaluated and has an effect on individual and team strategies. The weighted on base average (wOBA) is well known as a measure of an batter's hitting contribution. However, this measure ignores the game situation, such as the runners on base, which coaches and batters are known to consider when employing multiple hitting strategies. Yet, the effectiveness of these strategies is unknown. This is probably because (1) we cannot obtain the batter's strategy and (2) it is difficult to estimate the effect of the strategies. Here, we propose a new method for estimating the effect using counterfactual batting simulation. To this end, we propose a deep learning model that transforms batting ability when batting strategy is changed. This method can estimate the effects of various strategies, which has been traditionally difficult with actual game data. We found that, when the switching cost of batting strategies can be ignored, the use of different strategies increased runs. When the switching cost is considered, the conditions for increasing runs are limited. Our validation results suggest that our simulation could clarify the effect of using multiple batting strategies.</p>

    DOI: 10.11517/pjsai.jsai2022.0_3g4os15b04

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  41. Diversity of behavioral strategy in cooperative hunting using multi-agent deep reinforcement learning Reviewed

    TSUTSUI Kazushi, TAKEDA Kazuya, FUJII Keisuke

    Proceedings of the Annual Conference of JSAI   Vol. JSAI2022 ( 0 ) page: 3G4OS15b03 - 3G4OS15b03   2022

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    <p>Cooperative hunting is a widespread form of cooperation in nature, and it is known that the level of organization of this predation varies among species. However, how cooperative forms of predation have evolved and been maintained is not well understood. In this study, we addressed this issue using a multi-agent simulation based on deep reinforcement learning. We examined changes in behavioral strategies when changing factors that have been suggested to be associated with predation forms by previous observations in nature, and found that the highest level of organization with role division among individuals was emerged under the combined conditions of two factors: difficulty of prey capture, and food (reward) sharing. These results suggest that sophisticated predation forms, which have been thought to require high cognition, can evolve from relatively simple cognitive and learning mechanisms, and emphasize the close link between the predation form and the environment where the organism lives.</p>

    DOI: 10.11517/pjsai.jsai2022.0_3g4os15b03

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  42. Player evaluation in a racket sport via deep reinforcement learning with technical and tactical contexts Reviewed

    DING Ning, TAKEDA Kazuya, FUJII Keisuke

    Proceedings of the Annual Conference of JSAI   Vol. JSAI2022 ( 0 ) page: 1S1IS304 - 1S1IS304   2022

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    <p>Evaluating the performance of players in dynamic competition plays a vital role in effective sports coaching. However, the evaluation of players in racket sports has been still difficult in a quantitative manner, because it is derived from the integration of complex tactical and technical (i.e., whole-body movement) performances. In this paper, we propose a new evaluation method for racket sports based on deep reinforcement learning, which can analyze the player's motion in more detail than the results (i.e., scores). Our method uses historical data including players' tactical and technical performance information to learn the next score probability as Q function, which is used to value players’ actions. We verified our approach by comparing various models and present the effectiveness of our method through use cases that analyze the performance of the top badminton players in world-class events.</p>

    DOI: 10.11517/pjsai.jsai2022.0_1s1is304

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  43. Analysis of group behavior based on sharing heterogeneous roles in a triad using a coordinated drawing task Reviewed

    Jun Ichikawa, Keisuke Fujii

    Frontiers in Psychology     2022

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  44. Cooperative play classification in team sports via semi-supervised learning, International Journal of Computer Science in Sport Reviewed

    Ziyi Zhang, Kazuya Takeda, Keisuke Fujii

    International Journal of Computer Science in Sport     2022

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  45. Frechet and Hausdorff Kernels for Trajectory Data Analysis Reviewed

    Koh Takeuchi, Masaaki Imaizumi, Shunsuke Kanda, Keisuke Fujii, Masakazu Ishihata, Takuya Maekawa, Ken Yoda, Yasuo Tabei

    29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2021)     2021.11

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  46. Skill Differences in a Discrete Motor Task Emerging From the Environmental Perception Phase Reviewed

    Yumiko Hasegawa, Ayako Okada, Keisuke Fujii

    Frontiers in Psychology   Vol. 12   2021.10

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    Because of the challenges associated with measuring human perception and strategy, the process of human performance from perception to motion to results is not fully understood. Therefore, this study clarifies the phase at which errors occur and how differences in skill level manifest in a motor task requiring an accurate environmental perception and fine movement control. We assigned a golf putting task and comprehensively examined various errors committed in five phases of execution. Twelve tour professionals and twelve intermediate amateur golfers performed the putting task on two surface conditions: flat and a 0.4-degree incline. The participants were instructed to describe the topographical characteristics of the green before starting the trials on each surface (environmental perception phase). Before each attempt, the participants used the reflective markers to indicate their aim point from which the ball would be launched (decision-making phase). We measured the clubface angle and impact velocity to highlight the pre-motion and motion errors (pre-motion and motion phase). In addition, mistakes in the final ball position were analyzed as result errors (post-performance phase). Our results showed that more than half of the amateurs committed visual–somatosensory errors in the perception phase. Moreover, their aiming angles in the decision-making phase differed significantly from the professionals, with no significant differences between slope conditions. In addition, alignment errors, as reported in previous studies, occurred in the pre-motion phase regardless of skill level (i.e., increased in the 0.4-degree condition). In the motion phase, the intermediate-level amateurs could not adjust their clubhead velocity control to the appropriate level, and the clubhead velocity and clubface angle control were less reproducible than those of the professionals. To understand the amateur result errors in those who misperceived the slopes, we checked the individual results focusing on the final ball position. We found that most of these participants had poor performance, especially in the 0.4-degree condition. Our results suggest that the amateurs’ pre-motion and strategy errors depended on their visual–somatosensory errors.

    DOI: 10.3389/fpsyg.2021.697914

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  47. Supervised sequential pattern mining of event sequences in sport to identify important patterns of play: An application to rugby union Reviewed

    Rory Bunker, Keisuke Fujii, Hiroyuki Hanada, Ichiro Takeuchi

    PLOS ONE   Vol. 16 ( 9 ) page: e0256329 - e0256329   2021.9

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    Given a set of sequences comprised of time-ordered events, sequential pattern mining is useful to identify frequent subsequences from different sequences or within the same sequence. However, in sport, these techniques cannot determine the importance of particular patterns of play to good or bad outcomes, which is often of greater interest to coaches and performance analysts. In this study, we apply a recently proposed supervised sequential pattern mining algorithm called safe pattern pruning (SPP) to 490 labelled event sequences representing passages of play from one rugby team’s matches in the 2018 Japan Top League season. We obtain patterns that are the most discriminative between scoring and non-scoring outcomes from both the team’s and opposition teams’ perspectives using SPP, and compare these with the most frequent patterns obtained with well-known unsupervised sequential pattern mining algorithms when applied to subsets of the original dataset, split on the label. From our obtained results, line breaks, successful line-outs, regained kicks in play, repeated phase-breakdown play, and failed exit plays by the opposition team were found to be the patterns that discriminated most between the team scoring and not scoring. Opposition team line breaks, errors made by the team, opposition team line-outs, and repeated phase-breakdown play by the opposition team were found to be the patterns that discriminated most between the opposition team scoring and not scoring. It was also found that, probably because of the supervised nature and pruning/safe-screening mechanisms of SPP, compared to the patterns obtained by the unsupervised methods, those obtained by SPP were more sophisticated in terms of containing a greater variety of events, and when interpreted, the SPP-obtained patterns would also be more useful for coaches and performance analysts.

    DOI: 10.1371/journal.pone.0256329

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  48. Flexible prediction of opponent motion with internal representation in interception behavior Reviewed

    Kazushi Tsutsui, Keisuke Fujii, Kazutoshi Kudo, Kazuya Takeda

    Biological Cybernetics     2021.8

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    <title>Abstract</title>Skilled interception behavior often relies on accurate predictions of external objects because of a large delay in our sensorimotor systems. To deal with the sensorimotor delay, the brain predicts future states of the target based on the current state available, but it is still debated whether internal representations acquired from prior experience are used as well. Here we estimated the predictive manner by analyzing the response behavior of a pursuer to a sudden directional change of the evasive target, providing strong evidence that prediction of target motion by the pursuer was incompatible with a linear extrapolation based solely on the current state of the target. Moreover, using neural network models, we validated that nonlinear extrapolation as estimated was computationally feasible and useful even against unknown opponents. These results support the use of internal representations in predicting target motion, suggesting the usefulness and versatility of predicting external object motion through internal representations.

    DOI: 10.1007/s00422-021-00891-9

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  49. Understanding Others' Roles Based on Perspective Taking in Coordinated Group Behavior Reviewed

    Jun Ichikawa, Keisuke Fujii

    Proceedings of the Annual Meeting of the Cognitive Science Society   Vol. 43   page: 1285 - 1291   2021.8

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  50. Extraction of swing motion contributing to prediction of shuttle drop position in badminton Reviewed

    Tatsuya Yoshikawa, Kazushi Tsutsui, Kazuya Takeda, Keisuke Fujii

    30th International Joint Conference on Artificial Intelligence (IJCAI-21) workshop on AI for Sports Analytics (AISA)     2021.8

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  51. Investigation of roles in group behavior using a coordinative drawing task Reviewed

    Ichikawa Jun, Fujii Keisuke

    Cognitive Studies: Bulletin of the Japanese Cognitive Science Society   Vol. 28 ( 1 ) page: 170 - 173   2021.3

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    DOI: 10.11225/cs.2020.069

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  52. Quantitative analysis of spontaneous sociality in children's group behavior during nursery activity. Reviewed International journal

    Jun Ichikawa, Keisuke Fujii, Takayuki Nagai, Takashi Omori, Natsuki Oka

    PloS one   Vol. 16 ( 2 ) page: e0246041   2021.2

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    Sociality is the tendency to spontaneously interact with others to establish and maintain relationships. Some approaches, including questionnaires, tests, controlled experiments, and qualitative field research, cannot capture complex social interactions, such as in children during nursery activities, because of problems with ecological validity and the labor cost of analysis. Here, we introduced a new methodology for the quantitative analysis of spontaneous social movement and investigated children's group behavior using position data. We periodically visited a nursery and recorded videos of eurhythmics, in which children move in tune with music, in different classes. The results revealed that children in the six-year-old class approached others in a short period of time (within one second) and established group behavior like that in a game of tag. It can be interpreted that such social behavior may include actions related to the cognition of anticipating others' behaviors in a complex situation. Although only a small amount of data could be acquired, this study suggests one of the characteristics of social behaviors in the classroom considering an ecological approach.

    DOI: 10.1371/journal.pone.0246041

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  53. Trajectory prediction with imitation learning reflecting defensive evaluation in team sports. Reviewed

    Masakiyo Teranishi, Keisuke Fujii, Kazuya Takeda

    IEEE 9th Global Conference on Consumer Electronics (GCCE 2020)     page: 124 - 125   2020.12

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    DOI: 10.1109/GCCE50665.2020.9291841

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  54. Cognition and interpersonal coordination of patients with schizophrenia who have sports habits. Reviewed International journal

    Keisuke Fujii, Yujiro Yoshihara, Yukiko Matsumoto, Keima Tose, Hideaki Takeuchi, Masanori Isobe, Hiroto Mizuta, Daisuke Maniwa, Takehiko Okamura, Toshiya Murai, Yoshinobu Kawahara, Hidehiko Takahashi

    PloS one   Vol. 15 ( 11 ) page: e0241863   2020.11

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    Team sports activities are effective for improving the negative symptoms and cognitive functions in patients with schizophrenia. However, the interpersonal coordination during the sports and visual cognition of patients with schizophrenia who have team sports habits are unknown. The main objectives of this study were to test two hypotheses: first, patients with schizophrenia perform the skill requiring ball passing and receiving worse than healthy controls; and second, the patients will be impaired in these functionings in accordance with the previous studies regarding schizophrenia in general. Twelve patients with schizophrenia and 15 healthy controls, who had habits in football, participated in this study. The participants performed three conventional cognitive tests and a 3-vs-1 ball possession task to evaluate their interpersonal coordination. The results showed that in the 3-vs-1 possession task, the displacement in the pass angle for the patients was significantly smaller than that for the control. The recall in the complex figure test, the performance in the trail making test, and that in the five-choice reaction task for the patients were worse than those for the control. Moreover, we found the significant partial correlations in the patients between the extradimensional shift error and the pass angle as well as between the time in the trail making test and the displacement in the pass angle, whereas there was no significant correlation in the control group. This study clarified the impaired interpersonal coordination during team sports and the visual cognition of patients with schizophrenia who have team sports habits.

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  55. Succinct Trit-array Trie for Scalable Trajectory Similarity Search. Reviewed

    Shunsuke Kanda, Koh Takeuchi, Keisuke Fujii, Yasuo Tabei

    Proceedings of 28th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2020)     page: 518 - 529   2020.11

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    DOI: 10.1145/3397536.3422210

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  56. Proposal of a research approach for discussion of a dynamic coordination mechanism: Investigation of anticipating others' behaviors and adaptation through quantitative analysis of group behavior Reviewed

    Ichikawa Jun, Fujii Keisuke

    Cognitive Studies: Bulletin of the Japanese Cognitive Science Society   Vol. 27 ( 3 ) page: 377 - 385   2020.9

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    <p>In this paper, we propose a research approach that is used in cognitive science to investigate more complex coordination mechanisms between three or more people. That is, we propose an approach that uses position data to quantitatively analyze group behavior and to link these characteristics with the cognition of anticipating others' behaviors. It is important for coordination to anticipate others' behaviors and to adapt one's own body movement to others based on anticipation. We highlight previous studies on problem solving and learning in cognitive science which have investigated interaction processes from verbal protocols during task implementation and indicated the importance of understanding others' perspectives. Additionally, recent cognitive models of estimating others' intentions and anticipating others' behaviors during interactions using non-verbal information such as eye movement, posture, and gesture, have been investigated. Considering these previous studies, we focus on group behavior and propose to apply the new approach mentioned above to discuss a mechanism of more complex coordination. We also refer to some studies of biological group behaviors in biology, artificial life, and sports science, and demonstrate a potential issue that such papers did not focus on the cognition related to coordinative group behaviors. This paper illustrates an example of discussing interactions with others, to which the new approach is applied. Our previous study here analyzed children's group behavior during nursery activities using position data and linked these characteristics with the cognitive development of anticipating others' behaviors based on spontaneous sociality. However, it is difficult to investigate some details of group behavior due to the limitation of field measurement, for example, the accuracy of a child's anticipation and whether a child moved based upon anticipation. In future work, it is important to analyze controlled group behavior and to indicate accuracy of individuals' anticipation from movement data to solve these problems.</p>

    DOI: 10.11225/cs.2020.026

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  57. COGNITION AND SOCIAL BEHAVIORS IN SPORTS FOR PATIENTS WITH SCHIZOPHRENIA ENGAGED IN SPORTS HABITS Reviewed

    Yoshihara Yujiro, Fujii Keisuke, Murai Toshiya, Takahashi Hidehiko

    SCHIZOPHRENIA BULLETIN   Vol. 46   page: S128 - S128   2020.4

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  58. Practice Motions Performed During Preperformance Preparation Drive the Actual Motion of Golf Putting Reviewed

    Yumiko Hasegawa, Akito Miura, Keisuke Fujii

    Frontiers in Psychology   Vol. 11   page: Article 513   2020.3

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    DOI: 10.3389/fpsyg.2020.00513

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  59. Practice Motions Performed During Preperformance Preparation Drive the Actual Motion of Golf Putting Reviewed

    Yumiko Hasegawa, Akito Miura, Keisuke Fujii

    Frontier in Psychology   Vol. 11 ( 513 ) page: https://doi.org/10.3389/fpsyg.2020.00513   2020.3

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    DOI: https://doi.org/10.3389/fpsyg.2020.00513

  60. Dynamic mode decomposition via dictionary learning for foreground modeling in videos Reviewed

    Israr Ul Haq, Keisuke Fujii, Yoshinobu Kawahara

    15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP'20)   Vol. 5 ( - ) page: 476-483   2020.3

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    DOI: 10.5220/0009144604760483

  61. Physically-interpretable classification of network dynamics for complex collective motions Reviewed

    Keisuke Fujii, Naoya Takeishi, Motokazu Hojo, Yuki Inaba, Yoshinobu Kawahara

    Scientific Reports   Vol. 10 ( 3005 ) page: https://doi.org/10.1038/s41598-020-58064-w   2020.2

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    DOI: https://doi.org/10.1038/s41598-020-58064-w

  62. 協調に関する議論に向けたアプローチの提案-集団運動からみる他者の行動予測と適応- Reviewed

    市川 淳, 藤井 慶輔

    認知科学   Vol. 27 ( 3 ) page: xx - xx   2020

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  63. Succinct Trie Index for Fast and Memory-Efficient Trajectory Similarity Search Reviewed

    KANDA Shunsuke, TAKEUCHI Koh, FUJII Keisuke, TABEI Yasuo

    Proceedings of the Annual Conference of JSAI   Vol. 2020 ( 0 ) page: 2C4OS7a01 - 2C4OS7a01   2020

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    <p>This paper proposes an efficient similarity search method for massive collections of trajectories. This method enables fast similarity searches by leveraging locality sensitive hashing and tries. To achieve memory efficiency, we propose two novel techniques of node reduction and a space-efficient representation for tries. We experimentally test our method on its ability to retrieve similar trajectories for a query from large collections of trajectories and show that our method performs superiorly with respect to search time and memory efficiency.</p>

    DOI: 10.11517/pjsai.JSAI2020.0_2C4OS7a01

  64. 「現場と研究の橋渡し」( 2 )にあたって

    藤井 慶輔, 永田 直也

    バスケットボール研究   Vol. 6 ( 0 ) page: 1 - 2   2020

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    DOI: 10.34396/jsbs.6.0_1

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  65. Motor control of practice and actual strokes by professional and amateur golfers differ but feature a distance-dependent control strategy. Reviewed International journal

    Yumiko Hasegawa, Keisuke Fujii, Akito Miura, Keiko Yokoyama, Yuji Yamamoto

    European journal of sport science   Vol. 19 ( 9 ) page: 1204 - 1213   2019.10

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    We explored how practice and actual putting strokes differed between professionals and high-level golf amateurs, and how practice strokes reflected subtle differences in putting distances. We analysed swing amplitude, impact velocity, and acceleration profile of the club-head. The acceleration profiles showed that the motor control pattern of the practice stroke differed from that of the actual stroke. To clarify the effects of different putting distances on the practice stroke and to analyse how much the actual stroke could be explained by the practice stroke, we conducted individual regression analyses. The practice strokes of all participants could be divided into three strategies and five types by the coefficient of determination and the slope. This implies that the purpose of the practice stroke varied among golfers. Most golfers used the individual velocity criteria in their practice strokes, which resulted in different putting distances based on their criteria. Unexpectedly, we found no significant difference in skill level between professionals and high-level amateurs. The results of this study imply that the practice stroke does not duplicate the actual stroke, even for professional golfers with excellent skills. However, most high-level golfers adopted distance-dependent control strategies for slightly different putting distances.

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  66. The kinetic mechanisms of vertical pointing movements. Reviewed International journal

    Shinji Yamamoto, Keisuke Fujii, Kisho Zippo, Keisuke Kushiro, Masanobu Araki

    Heliyon   Vol. 5 ( 7 ) page: e02012   2019.7

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    The present study utilized induced acceleration analysis to clarify the contributions of muscular and gravitational torques to the kinematics of vertical pointing movements performed by the upper limb. The study included eight healthy men with a mean age of 25 years. The experiment was divided into three blocks with ten trials in each, comprising five upward and five downward, randomly executed movements. The movements were recorded by a motion capture system and were subsequently analyzed. During the deceleration phase of the upward movement and the acceleration phase of the downward movement, the angular acceleration induced by gravitational torque contributed more to the generation of net induced angular acceleration than the angular acceleration induced by muscular torque. In addition, the difference between the net induced angular acceleration profiles during the upward and downward movements was mainly attributable to the difference between the respective angular acceleration profiles induced by muscular torque. These findings suggest that the central nervous system considers the gravitational effect on the upper limb in a phase-specific manner and accordingly generates a torque-derived kinematic difference with respect to the movement direction.

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  67. Analysis of factors predicting who obtains a ball in basketball rebounding situations Reviewed

    Motokazu Hojo, Keisuke Fujii, Yoshinobu Kawahara

    International Journal of Performance Analysis in Sport   Vol. 19 ( 2 ) page: 192 - 205   2019.2

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  68. Supervised Dynamic Mode Decomposition via Multitask Learning Reviewed

    Keisuke Fujii, Yoshinobu Kawahara

    Pattern Recognition Letters   Vol. 122 ( 1 ) page: 7 - 13   2019.2

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  69. 指導者から見た「研究と現場の橋渡し」

    陸川 章, 小山 孟志, 青木 美帆, 藤井 慶輔

    バスケットボール研究   Vol. 5 ( 0 ) page: 17 - 25   2019

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    DOI: 10.34396/jsbs.5.0_17

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  70. “Bridging the gap between the field and the laboratory”

    Fujii Keisuke, Nagata Naoya

    The Japan Journal of Basketball Studies   Vol. 5 ( 0 ) page: 1 - 2   2019

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    DOI: 10.34396/jsbs.5.0_1

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Books 17

  1. 実世界集団移動における機械学習を用いたデータ解析 自動車技術

    藤井 慶輔( Role: Contributor ,  78(1) 134-136)

    自動車技術会  2024.1 

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  2. 統計 特集 スポーツデータサイエンス

    藤井 慶輔( Role: Contributor ,  スポーツデータサイエンスの最近の研究分析の動向 ―機械学習を用いた集団スポーツのデータ解析を例に―)

    日本統計協会  2023.11 

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  3. 集団スポーツに関する機械学習を用いたデータ解析手法, 体育の科学

    藤井慶輔( Role: Contributor)

    杏林書院  2021.6 

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    Total pages:5   Responsible for pages:71(6) 403-407   Language:Japanese

  4. 実世界集団運動のデータ駆動科学, Society 5.0を支える未来のリーダー FACE the future, 計測と制御

    藤井慶輔( Role: Contributor ,  60(1), 2-3)

    計測自動制御学会  2021.1 

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  5. 集団スポーツに関するデータ駆動的解析手法, 特集=実用応用技術のシステム開発と応用, ケミカルエンジニヤリング

    藤井慶輔( Role: Contributor ,  65(9), 561-566)

    化学工業社  2020.9 

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  6. これからの集団運動科学の展望

    藤井 慶輔( Role: Sole author)

    杏林書院  2020.5 

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  7. これからの集団運動科学の展望, 連載 集団運動を科学する― 6, 体育の科学

    藤井慶輔( Role: Contributor ,  70(5), 349-353)

    杏林書院  2020.5 

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  8. 1対1の攻防における競合スキルの評価, 連載 集団運動を科学する― 5, 体育の科学

    筒井和詩, 藤井慶輔( Role: Contributor ,  70(4))

    杏林書院  2020.4 

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  9. 1対1の攻防における競合スキルの評価

    筒井 和詩, 藤井 慶輔( Role: Joint author)

    杏林書院  2020.4 

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  10. どこまで集団運動を解明できるのか

    藤井 慶輔( Role: Sole author)

    杏林書院  2019.12 

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  11. どこまで集団運動を解明できるのか, 連載 集団運動を科学する― 1, 体育の科学

    藤井 慶輔( Role: Contributor ,  69(12) 907-911)

    杏林書院  2019.12 

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  12. 特集 トレーニングを運動学習として捉える対人競技における学習された動きの評価

    藤井 慶輔, 山下大地( Role: Joint author ,  対人競技における学習された動きの評価)

    日本トレーニング科学会  2019.2 

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  13. バスケットボールが科学で強くなる! 小谷 究, 柏倉 秀徳 (監修)

    藤井慶輔( Role: Contributor ,  3章 ディフェンスの科学)

    日東書院  2019 

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  14. バスケットボール学入門

    藤井 慶輔( Role: Joint author ,  13章 方法学)

    流通経済大学出版会  2017.11 

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  15. バスケットボール用語事典

    藤井 慶輔( Role: Joint author)

    廣済堂出版  2017.6 

  16. ヒト集団の競合・協働を研究する際の注意点-集団スポーツを例として-

    藤井 慶輔( Role: Joint author)

    体育の科学(杏林書院)  2016.10 

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  17. なぜ陸上のトラックは左回りなの?/なぜ速く走るときは腕を振るの?

    藤井 慶輔( Role: Joint author)

    ヒトの動き百話(市村出版)  2011 

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    Total pages:4   Language:Japanese

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MISC 12

  1. Motor control of practice and actual strokes by professional and amateur golfers differ but feature a distance-dependent control strategy Reviewed

    Hasegawa Y, Fujii K, Miura A, Yokoyama K, Yamamoto Y

    European journal of sport science   Vol. 19 ( 9 ) page: 1204-1213   2019.10

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    DOI: 10.1080/17461391.2019.1595159

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  2. Supervised Dynamic Mode Decomposition via Multitask Learning Reviewed

    Keisuke Fujii, Yoshinobu Kawahara

    Pattern Recognition Letters   Vol. 122 ( 1 ) page: 7-13   2019.2

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  3. Analysis of factors predicting who obtains a ball in basketball rebounding situations Reviewed

    Motokazu Hojo, Keisuke Fujii, Yoshinobu Kawahara

    International Journal of Performance Analysis in Sport   Vol. 19 ( 2 ) page: 192-205   2019.2

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  4. 横-トレーニング-06 機械学習による集団スポーツの選手の動きの分類・予測手法を利活用する方法の提案

    藤井 慶輔, 方城 素和, 元安 陽一, 稲葉 優希

    日本体育学会大会予稿集   Vol. 70 ( 0 ) page: 77_3 - 77_3   2019

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    <p> 近年では全選手とボールの位置を計測するシステムがプロスポーツにおいて導入され、移動距離などのフィットネス指標や、特定のプレーの空間分布などの情報を得られるようになった。しかし、依然として戦術面に関しては、シュートなどある時点での空間分布の分析もしくはビデオベースの人の目視による分析が主であり、複数の選手の動きを考慮したプレーに関する自動分類・予測手法についてはほとんど利用されていない。そこで我々は、機械学習技術を用いてバスケットボールの集団攻撃プレーを自動分類する手法や、リバウンドと呼ばれるシュート後にボールを獲得する選手や得点を予測する手法を開発した。これらの方法によって、これまで経験のあるスタッフが時間を掛けて分類・評価してきた作業を自動で行うことにより、作業の負担を軽減することが期待できる。また、そのプレーに貢献する重要な選手の動きを、プレーの経験を必要とせずに根拠をもって定量的に説明することが期待できる。発表当日では、より具体的にこれらの自動分類・予測手法を練習などで利活用する方法を提案する。</p>

    DOI: 10.20693/jspehss.70.77_3

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  5. Motor control of practice and actual strokes by professional and amateur golfers differ but feature a distance-dependent control strategy Reviewed

    Yumiko Hasegawa, Keisuke Fujii, Akito Miura, Keiko Yokoyama, Yuji Yamamoto

    European Journal of Sport Science   Vol. Accepted   page: .   2019

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  6. 対人競技における学習された動きの評価 (特集 トレーニングを運動学習として捉える)

    藤井 慶輔, 山下 大地

    Journal of training science for exercise and sport = トレーニング科学   Vol. 31 ( 1 ) page: 23 - 29   2019

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  7. Automatically recognizing strategic cooperative behaviors in various situations of a team sport Reviewed

    Motokazu Hojo, Keisuke Fujii, Yuki Inaba, Yoichi Motoyasu,Yoshinobu Kawahara

    PLoS One   Vol. 13 ( 12 ) page: e0209247   2018.12

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  8. Metric on nonlinear dynamical systems with Koopman operators Reviewed

    Isao Ishikawa, Keisuke Fujii, Masahiro Ikeda, Yuka Hashimoto, Yoshinobu Kawahara

    Advances in Neural Information Processing Systems 31 (NeurIPS'18)   Vol. 31   page: 2858-2868   2018.12

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  9. Prediction and classification in equation-free collective motion dynamics Reviewed

    Keisuke Fujii, Takeshi Kawasaki, Yuki Inaba, Yoshinobu Kawahara

    PLoS Computational Biology   Vol. 14 ( 11 ) page: e1006545   2018.11

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  10. Quantitative analysis and visualization of children's group behavior from the perspective of development of spontaneity and sociality Reviewed

    Jun Ichikawa, Keisuke Fujii, Takayuki Nagai, Takashi Omori, Natsuki Oka

    24th Conference of Collaboration Researchers International Working Group (CRIWG'18)     page: 169-176   2018.9

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  11. Social forces for team coordination in ball possession game Reviewed

    Keiko Yokoyama, Hiroyuki Shima, Keisuke Fujii, Noriyuki Tabuchi, Yuji Yamamoto

    Physical Review E   Vol. 97 ( 022410 ) page: .   2018.2

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  12. 競技レベルの高い相手との試合中におけるバスケットボール選手の運動出力と心拍応答 Reviewed

    藤井 慶輔, 小山 孟志

    スポーツパフォーマンス研究   Vol. 9   page: 542-556   2018.1

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Presentations 90

  1. Evaluation of Team Defense Positioning by Computing Counterfactuals using StatsBomb 360 data,

    Rikuhei Umemoto, Keisuke Fujii

    StatsBomb Conference  2023.10 

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    Language:English   Presentation type:Oral presentation (general)  

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  2. Defensive team analysis in the 2022 World Cup based on the event prediction

    Rikuhei Umemoto, Kazushi Tsutsui, Keisuke Fujii

    MathSport International  2023.7 

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  3. Visual analysis of control area in badminton doubles using drone video dataset

    Ding Ning, Kazuya Takeda, Yingjiu Bei, Keisuke Fujii

    MathSport International  2023.7 

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  4. Prediction of shot type and hit location based on pose information using badminton match videos

    Tatsuya Yoshikawa, Ding Ning, Kazuya Takeda, Keisuke Fujii

    MathSport International  2023.7 

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  5. Learning Interaction Rules from Multi-Animal Trajectories Invited International conference

    Keisuke Fujii

    2nd International Workshop on Behavior analysis and Recognition for knowledge Discovery (BiRD 2023) in conjunction with the IEEE International Conference on Pervasive Computing and Communications (PerCom 2023)  2023.3.13 

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    Event date: 2023.3

    Language:English   Presentation type:Oral presentation (invited, special)  

    Country:United States  

  6. 集団スポーツの戦術的な動きを機械学習の予測に基づき評価する Invited

    藤井 慶輔

    Meet up Chubu×A-idea(社会課題・地域活性化)  2023.3.9  愛知県、中部経済産業局、中部経済連合会

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    Event date: 2023.3

    Language:Japanese   Presentation type:Public lecture, seminar, tutorial, course, or other speech  

  7. 最新のスポーツアナリティクス研究論文はどこで読めるか? Invited

    藤井 慶輔

    Sports Analytics Research Platform (SARP)キックオフイベント  2023.1.26  一般社団法人日本スポーツアナリスト協会

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    Event date: 2023.1

    Language:Japanese   Presentation type:Public lecture, seminar, tutorial, course, or other speech  

  8. サッカーにおけるイベント予測に基づく一般化されたチームの守備評価

    梅基陸平, 中原啓, 筒井和詩, 藤井慶輔

    第12回日本統計学会スポーツ統計分科会スポーツデータ解析コンペティション  2023.1 

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    Event date: 2023.1

    Language:Japanese   Presentation type:Oral presentation (general)  

  9. Learning Interaction Rules from Small Amount of Multi-Agent Trajectories Invited International conference

    Keisuke Fujii

    The 4th TMI International Seminar (Nagoya University & Université Libre de Bruxelles Joint Event)  2022.12.1 

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    Event date: 2022.12

    Language:English   Presentation type:Oral presentation (invited, special)  

  10. 複雑な運動系列データを扱うための情報処理技術 Invited

    藤井 慶輔

    第22回名古屋大学-NTT技術交流会  2022.11.10 

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    Event date: 2022.11

    Language:Japanese   Presentation type:Oral presentation (invited, special)  

  11. スポーツ戦術をAIのデータ解析で評価する Invited

    藤井 慶輔

    あいちサイエンスフェスティバル2022  2022.11.8 

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    Event date: 2022.11

    Language:Japanese   Presentation type:Public lecture, seminar, tutorial, course, or other speech  

  12. Learning multi-agent rules from real-world trajectory data Invited International conference

    Keisuke Fujii

    3rd Joint ERCIM - JST Workshop  2022.10.20 

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    Event date: 2022.10

    Language:English   Presentation type:Oral presentation (invited, special)  

    Country:France  

  13. 集団運動における機械学習を用いたデータ科学 Invited

    藤井 慶輔

    第121回人工知能基本問題研究会(SIG-FPAI)  2022.9.28 

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    Event date: 2022.9

    Language:Japanese   Presentation type:Oral presentation (invited, special)  

  14. 集団スポーツの動きに関するデータ分析の導入と概要/手法と今後の展望 Invited

    藤井 慶輔

    大阪体育大学スポーツ科学研究科 スポーツ科学セミナー  2022.9.26 

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    Event date: 2022.9

    Language:Japanese   Presentation type:Public lecture, seminar, tutorial, course, or other speech  

  15. 集団スポーツに関する機械学習を用いたデータ解析手法 Invited

    藤井 慶輔

    第77回 日本体力医学学会シンポジウム「データから行動パフォーマンスを読み解くとは?:フィールドとアカデミックを繋ぐ行動アナリティクスの世界」  2022.9.23 

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    Event date: 2022.9

    Language:Japanese   Presentation type:Oral presentation (invited, special)  

  16. 生体集団運動における軌道予測モデルを用いた評価 Invited

    藤井 慶輔

    第21回認知的コミュニケーションワークショップ  2022.9.12 

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    Event date: 2022.9

    Language:Japanese   Presentation type:Oral presentation (invited, special)  

  17. 機械学習を用いた集団動態におけるデータ解析 Invited

    藤井 慶輔

    2022年度数理生物学会年会 企画シンポジウム「集団動態の数理モデルと情報解析」  2022.9.5  数理生物学会

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    Event date: 2022.9

    Language:Japanese   Presentation type:Oral presentation (invited, special)  

  18. スポーツ戦術をAIのデータ解析で評価する -AIで可能になる新しいスポーツ分析の世界- Invited

    藤井 慶輔

    名古屋大学情報学部・情報学研究科オンライン公開セミナー「「楽しい!」をめぐる情報学」  2022.9.3  名古屋大学情報学部・情報学研究科

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    Event date: 2022.9

    Language:Japanese   Presentation type:Public lecture, seminar, tutorial, course, or other speech  

  19. Evaluating a third base coach's decision making via game theory and machine learning

    Hiroshi Nakahara, Kazuya Takeda and Keisuke Fujii

    MathSport International  2022.7 

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    Event date: 2022.7

    Language:English   Presentation type:Oral presentation (general)  

  20. Automatic screen-play classification in basketball via semi-supervised learning,

    Ziyi Zhang, Kazuya Takeda and Keisuke Fujii

    MathSport International  2022.7 

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    Event date: 2022.7

    Language:English   Presentation type:Oral presentation (general)  

  21. Automatic screen-play classification in basketball via semi-supervised learning,

    Ziyi Zhang, Kazuya Takeda, Keisuke Fujii

    MathSport International  2022.7 

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    Event date: 2022.7

    Language:English   Presentation type:Oral presentation (general)  

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  22. 野生コウモリの出巣時における移動パターン計測及びグレンジャー因果を用いた群行動メカニズムに関する基礎的検討

    牛尾 和嵯, 藤岡 慧明, 藤井 慶輔, 波部 斉, 川嶋 宏彰, 飛龍 志津子

    2022年度人工知能学会全国大会(第36回)   2022.6.16 

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    Event date: 2022.6

    Language:Japanese   Presentation type:Oral presentation (general)  

  23. 軌道予測に基づいた味方の得点機会を創出するサッカー選手の評価

    寺西真聖, 筒井 和詩, 武田 一哉, 藤井 慶輔

    2022年度人工知能学会全国大会(第36回)  2022.6.16 

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    Event date: 2022.6

    Language:Japanese   Presentation type:Oral presentation (general)  

  24. 反実仮想シミュレーションを用いた野球におけるチーム打撃戦略の効果検証

    中原啓, 筒井 和詩, 武田 一哉, 藤井 慶輔

    2022年度人工知能学会全国大会(第36回)   2022.6.16 

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    Event date: 2022.6

    Language:Japanese   Presentation type:Oral presentation (general)  

  25. マルチエージェント深層強化学習を用いた協調的狩りにみられる行動方略の多様性 Invited

    筒井 和詩, 武田 一哉, 藤井 慶輔

    2022.6.16 

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    Event date: 2022.6

    Language:Japanese   Presentation type:Oral presentation (general)  

  26. スマートフォンカメラ動画を用いた競歩の反則判定

    鈴木智大, 武田一哉, 藤井慶輔

    第4回彗ひろば(バイオメカニクス研究会)  2022.6 

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    Event date: 2022.6

    Language:Japanese   Presentation type:Oral presentation (general)  

  27. Ding Ning, Kazuya Takeda and Keisuke Fujii

    Player evaluation in a racket sport via deep reinforcement learning with technical and tactical contexts

    The 36th Annual Conference of the Japanese Society for Artificial Intelligence (JSAI 2022)  2022.6 

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    Event date: 2022.6

    Language:English   Presentation type:Oral presentation (general)  

  28. Team sports analytics using AI Invited International conference

    Keisuke Fujii

    Sport Event Taiwan Workshop 2022 Course  2022.5.4 

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    Event date: 2022.5

    Language:English   Presentation type:Public lecture, seminar, tutorial, course, or other speech  

    Country:China  

  29. 機械学習を⽤いた3塁ベースコーチの判断評価

    中原 啓、武田 一哉、藤井 慶輔

    日本野球科学研究会  2021.11.27 

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    Event date: 2021.11

    Language:Japanese   Presentation type:Poster presentation  

  30. 生物集団の軌跡から相互作用の規則を学習するための拡張行動モデル

    藤井慶輔, 武石直也, 筒井和詩, 藤岡慧明, 西海望, 田中良弥, 福代三華, 井出薫, 河野裕美, 依田憲, 高橋晋, 飛龍志津子, 河原吉伸

    第24回 情報論的学習理論ワークショップ (IBIS 2021)  2021.11.12 

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    Event date: 2021.11

    Language:Japanese   Presentation type:Oral presentation (general)  

  31. 生体集団運動における学習ベースのモデルを用いた理解 Invited

    藤井 慶輔

    第20回認知的コミュニケーションワークショップ  2021.9.28 

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    Event date: 2021.9

    Language:Japanese   Presentation type:Symposium, workshop panel (nominated)  

  32. Supervised sequential pattern mining for identifying important patterns of play in rugby International conference

    Rory Bunker, Keisuke Fujii, Hiroyuki Hanada & Ichiro Takeuchi

    MathSport International  2021.6.25 

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    Event date: 2021.6

    Language:English   Presentation type:Oral presentation (general)  

  33. Evaluation of soccer team defense based on ball recovery and being attacked International conference

    Kosuke Toda, Masakiyo Teranishi, Keisuke Kushiro & Keisuke Fujii

    MathSport International  2021.6.24 

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    Event date: 2021.6

    Language:English   Presentation type:Oral presentation (general)  

  34. バドミントンにおけるシャトル落下位置予測に寄与するスイング動作の抽出

    吉川達也 、藤井慶輔、武田一哉

    第2回彗ひろば(バイオメカニクス研究会)  2021.6.19 

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    Event date: 2021.6

    Language:Japanese   Presentation type:Oral presentation (general)  

  35. 予測に基づく集団行動系列の評価:サッカーのチーム守備への適用

    戸田 康介, 寺西 真聖, 久代 恵介, 藤井 慶輔

    2021年度人工知能学会全国大会(第35回)  2021.6.9 

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    Event date: 2021.6

    Language:Japanese   Presentation type:Oral presentation (general)  

  36. 集団スポーツにおける機械学習を用いたデータ駆動科学 Invited

    藤井 慶輔

    第18回認知的コミュニケーションワークショップ  2019.9.16 

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    Event date: 2019.9

    Language:Japanese   Presentation type:Oral presentation (invited, special)  

    Country:Japan  

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  37. 機械学習による集団スポーツの選手の動きの分類・予測手法を利活用する方法の提案

    藤井慶輔, 方城素和, 元安陽一, 稲葉優希

    日本体育学会第70回大会  2019.9.10 

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    Event date: 2019.9

    Language:Japanese   Presentation type:Poster presentation  

    Country:Japan  

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  38. Dynamic mode decomposition and its applications (II) Invited International conference

    Keisuke Fujii

    2019 Distinguished Lecture and International Interdisciplinary Workshop  2019.8.6 

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    Event date: 2019.8

    Language:English   Presentation type:Oral presentation (invited, special)  

    Country:Korea, Republic of  

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  39. Dynamic mode decomposition and its applications (I) Invited International conference

    Keisuke Fujii

    2019 Distinguished Lecture and International Interdisciplinary Workshop  2019.8.5 

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    Event date: 2019.8

    Language:English   Presentation type:Oral presentation (invited, special)  

    Country:Korea, Republic of  

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  40. Data-driven spectral analysis for social biomechanics

    Keisuke Fujii, Naoya Takeishi, Yuki Inaba, Benio Kibushi, Motoki Kouzaki, Yoshinobu Kawahara

    1st Kei-Hiroba (Biomechanics Research Society)  2019.6.15 

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    Event date: 2019.6

    Language:English   Presentation type:Oral presentation (general)  

    Country:Japan  

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  41. 観測量間の動的構造を抽出するベクトル値再生核ヒルベルト空間における動的モード分解 International conference

    藤井 慶輔、河原 吉伸

    第21回情報論的学習理論ワークショップ(IBIS2018) 

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    Event date: 2018.11

    Language:Japanese   Presentation type:Poster presentation  

    Country:Japan  

  42. 集団スポーツを例としたヒト集団の競合や協働に関する研究 Invited International conference

    藤井 慶輔

    運動制御学セミナー 

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    Event date: 2018.3

    Language:Japanese   Presentation type:Oral presentation (invited, special)  

    Venue:大阪大学   Country:Japan  

  43. 集団スポーツの動きの情報処理とそのモデル化 Invited International conference

    藤井 慶輔

    映像情報メディア学会 冬季大会 2017 

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    Event date: 2017.12

    Language:Japanese   Presentation type:Oral presentation (invited, special)  

    Country:Japan  

  44. Human creative interaction of group sports: Information-based modeling Invited

    Keisuke Fujii

    The fourth international workshop on Skill Science 

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    Event date: 2017.11

    Language:English   Presentation type:Oral presentation (invited, special)  

    Country:Japan  

  45. 集団スポーツを例とした現象の主体と観測者から見た認知過程 Invited International conference

    藤井 慶輔

    日本認知科学会第34回大会 

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    Event date: 2017.9

    Language:Japanese   Presentation type:Oral presentation (invited, special)  

    Country:Japan  

  46. Identification of multi-player cooperative play by machine learning using group sports position information data International conference

    Keisuke Fujii

    Identification of multi-player cooperative play by machine learning using group sports position information data 

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    Event date: 2017.5

    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  47. 集団スポーツにおける自己と他者 Invited International conference

    藤井 慶輔

    第11回内部観測研究会 

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    Event date: 2017.2

    Language:Japanese   Presentation type:Oral presentation (invited, special)  

    Country:Japan  

  48. An attack-and-defend competition as a redundant cognitive-motor system

    Keisuke Fujii, Yuki Yoshihara, Yuji Yamamoto

    Society for Neuroscience 

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    Event date: 2016.11

    Language:English  

    Country:United States  

  49. 身体・個・群れとしての集団スポーツ Invited International conference

    藤井 慶輔

    群れシンポジウム(早稲田大学) 

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    Event date: 2016.10

    Language:Japanese   Presentation type:Oral presentation (invited, special)  

    Country:Japan  

  50. 集団スポーツにおける部分と全体から見たインタラクションに関する問題 Invited International conference

    藤井 慶輔

    第15回認知的コミュニケーションワークショップ 

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    Event date: 2016.9

    Language:Japanese   Presentation type:Oral presentation (invited, special)  

    Country:Japan  

  51. 大規模トラッキング・センサデータを利活用する手法の展望 Invited International conference

    藤井 慶輔

    第67回日本体育学会大会ランチョンセミナー 

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    Event date: 2016.8

    Language:Japanese  

    Country:Japan  

  52. トラッキング・センサデータを用いたバスケットボールにおけるチームプレー・対人スキルに関する研究報告とその展望 Invited International conference

    藤井 慶輔

    第2回日本バスケットボール学会サマーレクチャー 

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    Event date: 2016.8

    Language:Japanese  

    Country:Japan  

  53. 集団球技の巧みさとチームワークを通して身体的・社会的知能を理解する Invited International conference

    藤井 慶輔

    第1回脳情報学セミナー(静岡大学) 

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    Event date: 2016.5

    Language:Japanese   Presentation type:Oral presentation (invited, special)  

    Country:Japan  

  54. 集団スポーツの動きに関する仕組みと振舞い、あるいは部分と全体 Invited International conference

    藤井 慶輔

    第24回NS研究会(立命館大学) 

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    Event date: 2016.3

    Language:Japanese   Presentation type:Oral presentation (invited, special)  

    Venue:立命館大学   Country:Japan  

  55. Globally competitive and locally coordinative dynamics and behavior in group sports Invited

    Keisuke Fujii

    International Symposium Integrated Understanding for Emergent Property of Cooperation and Competition Dynamics 

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    Event date: 2016.3

    Language:English   Presentation type:Symposium, workshop panel (nominated)  

    Country:Japan  

  56. Helping behavior toward a teammate: real-time group problem-solving at multiple spatial scales during a ballgame

    Keisuke Fujii, Takeshi Koyama, Akira Rikukawa, Hiroshi Yamada, Yuji Yamamoto

    The First International Symposium on Swarm Behavior and Bio-Inspired Robotics 

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    Event date: 2015.10

    Language:English  

    Country:Japan  

  57. 実践的学習方法とその効果―スポーツ場面での全体-部分練習効果 Invited International conference

    藤井 慶輔

    第79回日本心理学会チュートリアルワークショップ 

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    Event date: 2015.9

    Language:Japanese   Presentation type:Symposium, workshop panel (nominated)  

    Country:Japan  

  58. Interpersonal competition dynamics in a ballgame: Modeling of a 1-on-1 game and two opposing cognitive systems International conference

    Keisuke Fujii, Yuji Yamamoto

    International Society of Posture and Gait Research 

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    Event date: 2015.6

    Language:English   Presentation type:Oral presentation (general)  

    Country:Spain  

  59. The role of the kinetic preparatory state in defending against a dribbler in a basketball 1-on-1 subphase

    Keisuke Fujii, Shinsuke Yoshioka, Tadao Isaka, Motoki Kouzaki

    The Asian-South Pacific Association of Sport Psychology (ASPASP) 

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    Event date: 2014.8

    Language:English   Presentation type:Oral presentation (general)  

    Country:United States  

  60. 選手らしい動きと部分的な観測に基づく選手軌道予測モデル

    藤井 慶輔, 武石 直也, 河原 吉伸, 武田 一哉

    日本バスケットボール学会第 7 回学会大会  2020.12.19 

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    Presentation type:Oral presentation (general)  

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  61. 部分観測過程におけるデータ駆動的エージェントモデリング Invited

    藤井 慶輔

    日本認知科学会第37回大会 OS2 認知的インタラクションフレームワークの構築  2020.9.16 

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  62. Data-driven Analysis for Multi-agent Trajectories in Team Sports

    Keisuke Fujii

    17th AIP Open Seminar, Talks by Structured Learning Team in RIKEN Advanced Intelligence Project  2021.3.17 

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  63. Automatic classification of offensive and defensive tactical plays in team sports

    Keisuke Fujii

    The 2020 Yokohama Sport Conference  2020.9.9 

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  64. サッカーにおけるボール奪取・被有効攻撃予測に基づくチームの守備評価

    戸田康介, 寺西真聖, 久代恵介, 藤井慶輔

    第10回日本統計学会スポーツ統計分科会スポーツデータ解析コンペティション  2020.12.27 

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  65. 集団運動におけるデータ駆動科学(企画セッション:データ駆動科学と機械学習) Invited

    藤井 慶輔

    第22回情報論的学習理論ワークショップ  2019.11.22 

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    Presentation type:Oral presentation (invited, special)  

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  66. 集団スポーツの戦術に関するデータ解析手法 Invited

    藤井 慶輔

    第6回愛媛大学DS研究セミナー  2021.3.18 

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    Presentation type:Public lecture, seminar, tutorial, course, or other speech  

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  67. 集団スポーツにおけるデータ駆動的モデルを用いた理解 Invited

    藤井 慶輔

    第19回認知的コミュニケーションワークショップ  2020.11.3 

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  68. Extraction of swing motion contributing to prediction of shuttle drop position in badminton International conference

    Tatsuya Yoshikawa, Kazushi Tsutsui, Kazuya Takeda and Keisuke Fujii

    AI for Sports Analytics (AISA) Workshop IJCAI 2021  2021.8.17 

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    Language:English   Presentation type:Oral presentation (general)  

  69. Multi-agent deep-learning based comparative analysis in basketball

    ZHANG Ziyi, BUNKER Rory, TAKEDA Kazuya, FUJII Keisuke

    Proceedings of the Annual Conference of JSAI  2023  The Japanese Society for Artificial Intelligence

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    <p>Analysis of multi-agent trajectories is one of the fundamental issues for understanding real-world biological movements. For trajectory analysis, combining with labels (e.g. scored or not in ballgames) can obtain insights rather than only from trajectories. However, the previous deep-learning based method used only single agent trajectory in animals and cannot be directly applied to multi-agent ballgame trajectories. In this paper, we propose a comparative analysis method to analyze multi-agent trajectories in basketball. We adopt a neural network approach using multi-agent motion characteristics (e.g., distances between agents and objects) as the input and based on an attention mechanism to automatically detect segments in trajectories that are characteristic of one group. It enables us to understand the difference between groups by highlighting segmented trajectories and which variables correlate with the labels. We verified our approach by comparing various baselines and demonstrated the effectiveness of our method through use cases that analyze the attacking plays in the NBA league data.</p>

    DOI: 10.11517/pjsai.jsai2023.0_3u1is304

    CiNii Research

  70. Force-based modeling of heterogeneous roles in coordinated behavior of a triad

    ICHIKAWA Jun, FUJII Keisuke

    Proceedings of the Annual Conference of JSAI  2023  The Japanese Society for Artificial Intelligence

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    <p>Humans often interact with others to achieve a group goal. Many studies on cognitive science, neuroscience, and sports science have investigated the coordination mechanisms and suggested the importance of role-sharing and adjustment using others' motor information. However, the adjustment process in the nonverbal behavior of a triad is not fully understood due to complex and dynamic interactions. We previously introduced a coordinated drawing task and conducted the behavioral experiment. The triads operated reels to change thread tensions, shared three heterogeneous roles (pulling, relaxing, and adjusting), and moved a pen connected to the three threads to draw an equilateral triangle. The results indicated that the adjusting role was related to high task performance, who helped resiliently without disturbing the pen's smooth movement while avoiding great pen deviation. To supplement these findings, this study formulated the three roles using equations of motion. The multi-agent simulation results showed that the adjusting role might use the degree of pen deviation reflected by others' motor information, such as the operating procedures and forces, and change the tension to draw at least three sides. The contribution of this study is to enhance the fundamental understanding of resilient adjustment required in team sports and haul seines.</p>

    DOI: 10.11517/pjsai.jsai2023.0_2u5is503

    CiNii Research

  71. 生体集団運動における機械学習を用いたインタラクションの計測と理解 Invited

    藤井 慶輔

    ヒューマンコミュニケーション基礎研究会(HCS 2024)  2024.3.3 

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  72. 深層強化学習に基づく連続状態空間におけるサッカーの複数選手の行動評価

    中原啓, 筒井 和詩, 武田 一哉, 藤井 慶輔

    2023年度人工知能学会全国大会(第37回)  2023.6 

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  73. 深層学習による高速かつラベルフリーなサッカーシーン検索

    内田 郁真, スコットアトム, 大西正輝, 藤井 慶輔, 亀田 能成

    2023年度人工知能学会全国大会(第37回)  2023.6 

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  74. 慣性センサとスマートフォンカメラを用いたフィギュアスケートにおけるエッジエラー判定

    田中諒汰, 鈴木智大, 武田一哉, 藤井慶輔

    第5回彗ひろば(バイオメカニクス研究会), 学生部門  2023.6 

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  75. 上方および後方視点のドローンビデオからの姿勢情報を用いたバドミントンダブルスのコントロール領域の推定

    丁寧, 武田一哉, Wenhui Jin, Yingjiu Bei, 藤井慶輔

    第21回情報学ワークショップ(WiNF 2023)  2023.12 

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  76. ゼブラフィッシュにおける群れ行動と小脳プルキンエ細胞の関係

    大湯 翼, 齋藤 魁登, 宮成 和浩, 佐藤 大我, 藤井 慶輔, 津田 佐知子

    第46回日本神経科学大会  2023.8 

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  77. サッカー中継映像を用いた姿勢推定に基づく反則予測

    方迦楽, Calvin Yeung, 藤井慶輔

    第21回情報学ワークショップ(WiNF 2023)  2023.12 

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  78. サッカーのイベント予測確率に基づく一般化された守備評価を用いた分析

    梅基陸平, 筒井 和詩, 藤井 慶輔

    2023年度人工知能学会全国大会(第37回)  2023.6 

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  79. イベント時空間予測と数理モデルに基づくサッカーの包括的スペース評価指標

    太田阿留, 藤井慶輔, 日置尋久

    2023年度スポーツデータサイエンスコンペティション サッカー部門  2024.1 

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  80. Wasserstein距離を用いたバスケットボール選手のシューティングスタイルのクラスタリングに基づくラインナップ分析

    山田和宏, 藤井慶輔

    SICE中部若手研究発表会2023  2023.11 

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  81. Team sports analytics using machine learning Invited

    Keisuke Fujii

    AI symposium at 29th Congress of International Society of Biomechanics (ISB)  2023.8.1 

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  82. Machine learning-based analysis with multi-agent trajectories

    Keisuke Fujii

    International Symposium of Hierarchical Bio-Navigation 2024\  2024.3.11 

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  83. Machine learning-based analysis using multi-agent trajectories

    Keisuke Fujii

    2023.9.11 

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  84. Exploration of Learning;Process in Cooperative Hunting with Deep Reinforcement Learning

    筒井 和詩, 田中良弥, 武田 一哉, 藤井 慶輔

    2023年度人工知能学会全国大会(第37回)  2023.6 

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  85. , 模倣学習を用いた障害物環境下における2種類のエコーロケーションコウモリの経路予測

    源田祥子, 手嶋優風, 小原大知, 青木耀大, 藤井慶輔, 飛龍志津子

    計測自動制御学会システム・情報部門学術講演会2023  2023.11 

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  86. 生体集団運動における科学的知識と機械学習の融合による理解 Invited

    藤井 慶輔

    液体・ガラスへのデータ駆動アプローチ - グラフニューラルネットワークとその周辺 -  2023.11.29 

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  87. 集団スポーツの動きを機械学習で評価する Invited

    藤井 慶輔

    神戸大学シンポジウム:運動・スポーツスキルの先端研究  2024.1.20 

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  88. 集団スポーツにおける機械学習を用いたデータ科学 Invited

    藤井 慶輔

    人工知能学会 知識ベースシステム研究会  2023.8.25 

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  89. 言語モデリングによる行動選択・状態推移確率の推定に基づく選手定量評価指標

    染谷大河, 川口康平, 藤井慶輔

    2023年度スポーツデータサイエンスコンペティション サッカー部門  2024.1 

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  90. 複数スポーツの多物体追跡データセットの構築と競技間における汎用性の検証

    スコットアトム, 内田 郁真, 丁寧, 梅基陸平, バンカーローリー, 小林蓮, 小山孟志, 大西正輝, 亀田 能成, 藤井 慶輔

    2023年度人工知能学会全国大会(第37回)  2023.6 

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Research Project for Joint Research, Competitive Funding, etc. 3

  1. バスケットボールの実際のゲーム中における動きの激しさの評価

    2016.4 - 2017.3

    スポーツチャレンジ研究助成 

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    Grant type:Competitive

  2. 球技のゲーム中に選手にかかる運動・生理的負荷の包括的理解

    2016.3 - 2017.4

    スポーツ学等研究助成 

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    Grant type:Competitive

  3. 相手がいる状況においてバスケットボール選手が素早く動き出すメカニズムの解明

    2014.4 - 2015.3

    スポーツチャレンジ研究助成 

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    Grant type:Competitive

KAKENHI (Grants-in-Aid for Scientific Research) 12

  1. 階層ナビゲーションのための数理・学習ベース解析手法と介入方策決定技術

    Grant number:21H05300  2021.9 - 2026.3

    科学研究費助成事業  学術変革領域研究(A)

    藤井 慶輔

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    Authorship:Principal investigator 

    Grant amount:\95940000 ( Direct Cost: \73800000 、 Indirect Cost:\22140000 )

  2. Hierarchical Bio-Navigation Integrating Cyber-Physical Space

    Grant number:21H05293  2021.9 - 2026.3

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Transformative Research Areas (A)

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    Authorship:Coinvestigator(s) 

  3. Cross-disciplinary research on the prediction and control of real-world interactions based on evidence and causality

    Grant number:21H04892  2021.4 - 2024.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (A)  Grant-in-Aid for Scientific Research (A)

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    Authorship:Coinvestigator(s) 

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  4. Easily available information technology based on the data-driven models for social biomechanics

    Grant number:20H04075  2020.4 - 2023.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B)  Grant-in-Aid for Scientific Research (B)

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    Authorship:Principal investigator 

    Grant amount:\16900000 ( Direct Cost: \13000000 、 Indirect Cost:\3900000 )

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  5. 目的志向的な相互作用を含む集団移動系列・経路の解析手法の開発

    Grant number:19H04941  2019.4 - 2021.3

    科学研究費助成事業  新学術領域研究(研究領域提案型)

    藤井 慶輔

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    Authorship:Principal investigator 

    Grant amount:\3770000 ( Direct Cost: \2900000 、 Indirect Cost:\870000 )

    複雑な動きをみせる生物などの群れでは、目的志向的な味方や外敵などの移動体との協力・逃避などの相互作用が起きている。一般に集団移動運動に関するデータ駆動的な解析手法においては、方法論的に明示的なモデルを仮定することが難しいため、未だ相互作用の性質を明らかにする決定的な方法論が確立されていない。そこで本研究では工学的な目標として、集団移動系列・経路データから相互作用を可視化して分類や予測を行う手法を開発し、その科学的な応用として、目的志向的な生物集団移動の機能や原理などを発見・理解することを目的とした研究を行う。
    本研究では工学的な目標として、集団移動系列・経路データから相互作用を可視化して分類や予測を行う手法を開発し、その科学的な応用として、目的志向的な生物集団移動の機能や原理などを発見・理解することを目的とする。当該年度は、複数人の移動軌跡の方策をモデリングするための部分観測と機械的制約による機械学習手法を開発した。この研究の目的は、生物学的制約を考慮した長期予測・操作可能な集団運動のシミュレーションを行うことにあり、部分観測過程と力学的制約を導入した分散型模倣学習モデルを提案した。その結果、バスケットボールやサッカーのような集団スポーツにて、正確な長期予測と観測を操作した反事実的予測が可能であることを示した。この研究は現在機械学習の国際会議に投稿中である。その他にも、スポーツ習慣のある統合失調症患者の認知機能と、3対1の対人協調の関係について明らかにした。この研究成果は、PLoS One誌に採択された[1]。その他にも、スポーツの戦術評価を反映した模倣学習による軌道予測に関する研究を行った。この研究は、国際会議IEEE 9th Global Conference on Consumer Electronics (GCCE 2020)に採択された。
    令和2年度が最終年度であるため、記入しない。
    令和2年度が最終年度であるため、記入しない。

  6. Technologies for visualizing social behaviors in multi-human motions with non-trivial behavioral rules

    Grant number:18K18116  2018.4 - 2021.3

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Early-Career Scientists

    Fujii Keisuke

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    Authorship:Principal investigator 

    Grant amount:\4290000 ( Direct Cost: \3300000 、 Indirect Cost:\990000 )

    The purpose of this project was to develop a technology for visualizing social behavior involving physical movement through data-driven modeling, and to create a basis for practical human use of the technology. As a result, we have developed a method that is useful for understanding multi-agent movements, even when using machine learning models with nonlinear structures that are generally difficult to interpret, such as by (1) extracting the mathematical structures behind them, (2) visualizing the learned representations, and (3) generating movements by modeling the components.

  7. チームワークの良さを支える神経基盤の解明

    Grant number:20H04087  2020.4 - 2023.3

    日本学術振興会  科学研究費助成事業 基盤研究(B)  基盤研究(B)

    水口 暢章, 藤井 慶輔, 福谷 充輝, 藤井 慶輔, 福谷 充輝

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    サッカーやバスケットボールなどのチームスポーツにおいて味方選手と協調して攻撃・守備をする能力、すなわちチーム戦術を遂行する能力は個人のスキルや身体能力と同様に勝敗を分ける重要な要素である。本研究では、チーム戦術遂行能力に関連する神経基盤を明らかにすることを目的としている。そのために、試合中の選手の動きを分析と磁気共鳴画像法を用いた脳画像解析を行う。本研究は、個人の神経学的特徴に合わせたチーム戦術トレーニング法の提案につながるなど応用研究としてもさらなる発展が期待できる。

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  8. 目的志向的な相互作用を含む集団移動系列・経路の解析手法の開発

    2019.4 - 2021.3

    日本学術振興会科学研究費  新学術領域研究(研究領域提案型) 

    藤井 慶輔

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    Authorship:Principal investigator  Grant type:Competitive

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  9. 支配法則が非自明なヒト集団運動における社会的行動の可視化技術

    2018.4 - 2020.3

    日本学術振興会科学研究費  若手研究 

    藤井 慶輔

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    Authorship:Principal investigator  Grant type:Competitive

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  10. 対人競技の巧みさの評価方法の開発:制御理論と力学系理論を相補的に用いて

    2016.4 - 2019.3

    日本学術振興会科学研究費  挑戦的萌芽研究  挑戦的萌芽研究

    藤井 慶輔

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    Authorship:Principal investigator  Grant type:Competitive

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  11. 球技を通して時空間マルチスケールな分岐現象を解明する

    2014.4 - 2017.1

    科学研究費補助金 

  12. 3次元倒立振子モデルを用いた方向転換走動作における予測・運動制御メカニズムの解明

    2011.4 - 2014.3

    科学研究費補助金 

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Industrial property rights 1

  1. 情報処理装置、情報処理方法、および、コンピュータプログラム

    藤井慶輔・寺西真聖・筒井和詩・武田一哉

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    Applicant:名古屋大学

    Application no:特願2021-116076 

    Date announced:2021.7

 

Teaching Experience (On-campus) 13

  1. Speech/Behavior Signal Processing 2

    2022

  2. Intelligent Systems h

    2022

  3. Intelligent Systems g

    2022

  4. Intelligent Systems f

    2022

  5. Intelligent Systems e

    2022

  6. Intelligent Systems d

    2022

  7. Intelligent Systems c

    2022

  8. Intelligent Systems b

    2022

  9. Intelligent Systems a

    2022

  10. Data Processing Tools2

    2021

  11. Speech/Behavior Signal Processing 2

    2021

  12. Mathematical Sciences 1,2 (Exercises)

    2020

  13. Speech/Behavior Signal Processing 2

    2020

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Social Contribution 3

  1. 【名古屋大学】データサイエンスで集団スポーツの解析に挑む

    Role(s):Media coverage

    株式会社ミニマル  データサイエンス百景  2023.9

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    Type:Internet

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  2. (名古屋大学)次世代リーダーのインタビュー

    Role(s):Appearance

    FM AICHI  中電シーティーアイ Welcome Generation  2023.8

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    Type:TV or radio program

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  3. サッカー戦術分析で起業へ 名大准教授、自動運転も応用

    Role(s):Media coverage

    日本経済新聞社  日本経済新聞、日経MJ  2023.5

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    Type:Newspaper, magazine

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Media Coverage 2

  1. <Meet STEAM>部活動の指導者不足の問題を解決へ 集団スポーツの「戦術」を科学的に評価する研究 Newspaper, magazine

    株式会社中日新聞社  中日新聞  https://www.chunichi.co.jp/article/656729?rct=manabu  2023.3

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    Author:Other 

  2. 名大・理研・JSTなど、生物集団の移動軌跡から相互作用の規則を理論とデータから推定できる機械学習技術を開発 Newspaper, magazine

    株式会社日本経済新聞社  日本経済新聞  https://www.nikkei.com/article/DGXLRSP623051_W1A201C2000000/  2021.12

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    Author:Myself