Publications(研究業績)

2023 学会発表 (Conference papers)

  1. Learnable Tegotae-based Feedback in CPGs with Sparse Observation Produces Efficient and Adaptive Locomotion, IEEE Int. Conf. on Robotics and Automation, May 2023, C. Herneth, M. Hayashibe, D. Owaki (ICRA2023)
  2. Morphological Characteristics That Enable Stable and Efficient Walking in Hexapod Robot Driven by Reflex-based Intra-limb Coordination, IEEE Int. Conf. on Robotics and Automation, May 2023, W. Sato, J. Nishii, M. Hayashibe, D. Owaki (ICRA2023)
  3. Synergy-based Motor Learning for Improving the Spatial and Temporal Generalization Ability, 9th IEEJ Int. Workshop on Sensing, Actuation, Motion Control, and Optimization, March 2023, K. Kutsuzawa, M. Hayashibe (SAMCON2023) (Invited talk)

2022  学術雑誌・論文誌 (Journal papers)

  1. Soft-Body Dynamics Induces Energy Efficiency in Undulatory Swimming: a Deep Learning Study, Frontiers in Robotics and AI, section Soft Robotics, (accepted), (2022), G. Li, J. Shintake and M. Hayashibe
  2. Multimodal Bipedal Locomotion Generation with Passive Dynamics via Deep Reinforcement Learning, Frontiers in Neurorobotics, (accepted), (2023), S. Koseki, K. Kutsuzawa, D. Owaki, M. Hayashibe
  3. Multibranch Convolutional Neural Network with Contrastive Representation Learning for Decoding Same Limb Motor Imagery Tasks, Frontiers in Human Neuroscience, 16:1032724, (2022), C. Phunruangsakao, D. Achanccaray, S. Izumi, M. Hayashibe
  4. Synergetic synchronized oscillation by distributed neural integrators to induce dynamic equilibrium in energy dissipation systems, Scientific Reports, 12, 17163, (2022), M. Hayashibe and S. Shimoda IF=4.99
  5. A Hierarchical Deep Reinforcement Learning Framework with High Efficiency and Generalization for Fast and Safe Navigation, IEEE Transactions on Industrial Electronics, (2022), W. Zhu and M. Hayashibe IF=8.16
  6. Prediction of Whole-Body Velocity and Direction From Local Leg Joint Movements in Insect Walking via LSTM Neural Networks, IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 9389-9396, (2022), Y. Wang, M. Hayashibe and D. Owaki (IROS22 Kyoto) IF=3.74
  7. Motor synergy generalization framework for new targets in multi-planar and multi-directional reaching task, Royal Society Open Science, vol. 9, 211721, (2022), K. Kutsuzawa and M. Hayashibe IF=3.65
  8. Deep Adversarial Domain Adaptation With Few-Shot Learning for Motor-Imagery Brain-Computer Interface, IEEE Access, vol. 10, pp. 57255-57265, (2022), C. Phunruangsakao, D. Achanccaray and M. Hayashibe
  9. Joint Elasticity Produces Energy Efficiency in Underwater Locomotion: Verification with Deep Reinforcement Learning, Frontiers in Robotics and AI, 9:957931, (2022), C. Zheng, G. Li, and M. Hayashibe
  10. Motion Hacking –Understanding by Controlling Animals–, J. Robot. Mechatron. 34(2) 301-303, (2022), Dai Owaki and Volker Dürr
  11. Editorial: Biological and Robotic Inter-Limb Coordination, Frontiers in Robotics and AI, 9:875493, (2022), Dai Owaki, Poramate Manoonpong, Amir Ayali

2022 学会発表 (Conference papers)

  1. Temporal Convolutional Network-based Gait Event Detection using IMU sensor, 33rd International Symposium on Micro-NanoMehatronics and Human Science, 2022, Y. Guo, Y. Hutabarat, D. Owaki, M. Hayashibe
  2. Deep Reinforcement Learning Based Motion Synthesis for Prosthetic Elbow Motion Generation, The SICE Annual Conference 2022, Sep 2022, M.H. Ahmed, S. Shimoda, M. Hayashibe (SICE2022)
  3. Quantifying Motor and Cognitive Function of the Upper Limb Using Mixed Reality Smartglasses, 44th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, July 2022, K. Tada, K. Kutsuzawa, D. Owaki, M. Hayashibe (EMBC2022)
  4. Game-Based Evaluation of Whole-Body Movement Functions with CoM Stability and Motion Smoothness, 44th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, July 2022, M. Kojima, K. Kutsuzawa, D. Owaki, M. Hayashibe (EMBC2022)
  5. Systematic Motion Integration with Multiple Depth Cameras Allowing Sensor Movement for Stable Skeleton Tracking, 44th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, July 2022, K. Furuhata, K. Kutsuzawa, D. Owaki, M. Hayashibe (EMBC2022)
  6. Temporal Variation Quantification During Cognitive Dual-Task Gait Using Two IMU Sensors, 44th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, July 2022, Y. Hutabarat, D. Owaki, M. Hayashibe (EMBC2022)
  7. Classification of Human Balance Recovery Strategies through Kinematic Motor Synergy Analysis, 44th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, July 2022, K. Shen, A. Chemori, M. Hayashibe (EMBC2022)
  8. A new Augmented L1 Adaptive Control for Wheel-Legged Robots: Design and Experiments, 2022 American Control Conference, June 2022, F. Raza, A. Chemori, M. Hayashibe (ACC2022)
  9. A Geometric Design Method for Variable Impedance Parameters in Assembly Tasks, 8th IEEJ Int. Workshop on Sensing, Actuation, Motion Control, and Optimization, March 2022, K. Kutsuzawa, M. Hayashibe (SAMCON2022)
  10. Reinforcement Learning Based Hierarchical Control for Path Tracking of a Wheeled Bipedal Robot with Sim-To-Real Framework, IEEE/SICE Int. Symposium on System Integration, Jan 2022, W. Zhu, F. Raza, M. Hayashibe (SII2022)
  11. 赤井田祐樹, 沓澤京, 大脇大, 林部充宏,“再帰型ニューラルネットワークによる手指運動の即時判別と後だしじゃんけんを用いた認知課題の定量化”, ロボティクス・メカトロニクス講演会(ROBOMECH2022), Jun 2022
  12. 松村拓海, 沓澤京, 大脇大, 林部充宏,“歩行運動の速度と負荷変化に対する時空間筋シナジー解析”, ロボティクス・メカトロニクス講演会(ROBOMECH2022), Jun 2022
  13. 前田真太郎, 菅井諒, 関口雄介, 大脇大, 林部充宏,“再帰型ニューラルネットワークを用いた深度カメラによる歩行動作中の床反力推定”, ロボティクス・メカトロニクス講演会(ROBOMECH2022), Jun 2022
  14. 平井虎太朗, 沓澤京, 大脇大, 林部充宏,“モデルベース強化学習を用いたヘビ型ロボットの環境適応性検証”, ロボティクス・メカトロニクス講演会(ROBOMECH2022), Jun 2022
  15. 杉山拓, 沓澤京, 大脇大, 林部充宏,“水圧両用駆動に向けたBending-type Fluidic Elastomer Actuatorの動作特性評価”, ロボティクス・メカトロニクス講演会(ROBOMECH2022), Jun 2022
  16. 多田憲矢, 沓澤京, 大脇大, 林部充宏, “Mixed Realityデバイスを用いた上肢の運動機能と認知機能の定量化に関する研究”, 計測自動制御学会 東北支部 第337回研究集会, Jun 2022 (優秀発表奨励賞)
  17. 福西彬仁, 沓澤京, 大脇大, 林部充宏, “筋骨格モデルにおけるモジュールを用いた異なる姿勢への適応能力の効果”, 計測自動制御学会 東北支部 第337回研究集会, Jun 2022 (優秀発表奨励賞)
  18. 福西彬仁, 沓澤京, 大脇大, 林部充宏, “筋骨格モデルによる異なる姿勢での等尺性力制御タスクの学習におけるモジュラリティ効果の検証”, 第16回Motor Control 研究会, Aug 2022
  19. 古関駿介, 沓澤京, 大脇大, 林部 充宏, “深層強化学習を用いた二脚モデルにおける歩容遷移の実現”, 第40回日本ロボット学会学術講演会, Sep 2022
  20. 沓澤京, 林部 充宏, “Time-varying Synergyを用いた動作の時空間的構造抽出による模倣学習”, 第40回日本ロボット学会学術講演会, Sep 2022
  21. 下村尚道, 沓澤京, 林部 充宏, “ビジョンシステムを用いた非均一な形状の平面物体の認識に関する研究”, 計測自動制御学会 東北支部 第338回研究集会, Sep 2022
  22. 古関駿介, 沓澤京, 大脇大, 林部充宏, “深層強化学習により獲得される二脚歩容遷移にみられるヒステリシス現象”, 計測自動制御学会 東北支部 第339回研究集会, Oct 2022 (優秀発表奨励賞)
  23. 佐藤渉, 西井淳, 林部充宏, 大脇大, “形態的特性を考慮した反射型六脚歩行制御モデルにおける耐故障性の検証”, 第23回計測自動制御学会SI部門講演会, Dec 2022
  24. 平井虎太朗, 沓澤京, 大脇大, 林部充宏, “モデルベース強化学習を用いたヘビ型ロボットの環境適応性に関する実験的検証”, 第23回計測自動制御学会SI部門講演会, Dec 2022
  25. 瀬宮優作, 沓澤京, 大脇大, 林部充宏, “脚ロボットにおける複数の歩行パターンの模倣に基づく遷移動作の学習”, 第23回計測自動制御学会SI部門講演会, Dec 2022
  26. 多田憲矢, 沓澤京, 大脇大, 林部充宏, “Mixed Reality デバイスを用いた動的リーチングタスクによる認知機能と運動機能の定量化”, 第23回計測自動制御学会SI部門講演会, Dec 2022
  27. 吉田高志, 沓澤京, 大脇大, 林部充宏, “モデルベース強化学習により生成された速度の異なる歩行運動パターンに共通するシナジー発現特性の検証”, 第23回計測自動制御学会SI部門講演会, Dec 2022

2021  学術雑誌・論文誌 (Journal papers)

  1. Spiking Neural Network Discovers Energy-efficient Hexapod Motion in Deep Reinforcement Learning, IEEE Access, Vol.9, pp.150345 – 150354, (2021), K. Naya, K. Kutsuzawa, D. Owaki, M. Hayashibe
  2. A Survey of Sim-to-Real Transfer Techniques applied to Reinforcement Learning for Bio-Inspired Robots, IEEE Transactions on Neural Networks and Learning Systems, (2021), W. Zhu, X. Guo, D. Owaki, K. Kutsuzawa, M. Hayashibe (accepted) IF=10.4
  3. Grey-box modeling and hypothesis testing of functional near-infrared spectroscopy-based cerebrovascular reactivity to anodal high-definition tDCS in healthy humans, PLOS Computational Biology, 17(10), e1009386, (2021), Y. Arora, P. Walia, M. Hayashibe, M. Muthalib, S.R. Chowdhury, S. Perrey, A. Dutta IF=4.7
  4. An Optimal Transport based Transferable System for Detection of Erroneous Somato-sensory Feedback from Neural Signals, Brain Sciences, 11(11), 1393, (2021), S. Bhattacharyya, M. Hayashibe
  5. Control Strategies for Gait Tele-Rehabilitation System Based on Parallel Robotics, Applied Sciences, 11(23), 11095, (2021), Antonio P.L. Bo, L. Casas, G. Cucho-Padin, M. Hayashibe, Dante A. Elias
  6. Recent Advances in Quantitative Gait Analysis using Wearable Sensors: A Review, IEEE Sensors, vol.21, pp.26470 – 26487, (2021), Y. Hutabarat, D. Owaki, M. Hayashibe
  7. Individual Deformability Compensation of Soft Hydraulic Actuators through Iterative Learning-Based Neural Network, Bioinspiration & Biomimetics, vol.16, 056016, (2021), T. Sugiyama, K. Kutsuzawa, D. Owaki, M. Hayashibe
  8. Adaptive and Energy-efficient Optimal Control in CPGs through Tegotae-based Feedback, Frontiers in Robotics and AI, 8:632804, (2021), R. Zamboni, D. Owaki, M. Hayashibe
  9. Synergy Emergence in Deep Reinforcement Learning for Full-dimensional Arm Manipulation, IEEE Transactions on Medical Robotics and Bionics, vol.3, no.2, pp.498-509, (2021), J. Han, J. Chai, M. Hayashibe
  10. Reproducing Human Arm Strategy and its Contribution to Balance Recovery Through Model Predictive Control, Frontiers in Neurorobotics, 15:679570, (2021), K. Shen, A. Chemori, M. Hayashibe
  11. Balance Stability Augmentation for Wheel-legged Biped Robot through Arm Acceleration Control, IEEE Access, Vol.9, pp.54022-54031, (2021), F. Raza, W. Zhu, M. Hayashibe
  12. Visual-Electrotactile Stimulation Feedback to Improve Immersive Brain-Computer Interface Based on Hand Motor Imagery, Computational Intelligence and Neuroscience, 8832686, (2021), D. Achanccaray, S. Izumi, M. Hayashibe
  13. An Extended Statically Equivalent Serial Chain – Identification of Whole Body Center of Mass with Dynamic Motion, Gait & Posture, Vol. 84, pp. 45-51, (2021), A. Gonzalez, P. Fraisse, M. Hayashibe
  14. Reinforcement Learning for Robotic Assembly Using Non-Diagonal Stiffness Matrix, IEEE Robotics and Automation Letters, 6(2):2737-2744, 2021, M. Oikawa, T. Kusakabe, K. Kutsuzawa, S. Sakaino, T. Tsuji
  15. Wearable Vibration Sensor for Measuring the Wing Flapping of Insects, Sensors, Vol. 21, No. 593, (2021), R. Yanagisawa, S. Shigaki, K. Yasui, D. Owaki, Y. Sugimoto, A. Ishiguro, M. Shimizu
  16. Leg amputation modifies coordinated activation of the middle leg muscles in the cricket Gryllus bimaculatus, Scientific Reports, 11, 1327, (2021), D. Owaki, H. Aonuma, Y. Sugimoto, A. Ishiguro IF=4.99
  17. A Comparative Study of Adaptive Interlimb Coordination Mechanisms for Self-Organized Robot Locomotion, Frontiers in Robotics and AI, 8:638684, (2021), Sun T, Xiong X, Dai Z, Owaki D and Manoonpong P
  18. Tegotae-Based Control Produces Adaptive Inter- and Intra-limb Coordination in Bipedal Walking, Frontiers in Neurorobotics, 15:629595, (2021), Owaki D, Horikiri S, Nishii J and Ishiguro A
  19. Two-Week Rehabilitation with Auditory Biofeedback Prosthesis Reduces Whole Body Angular Momentum Range during Walking in Stroke Patients with Hemiplegia: A Randomized Controlled Trial, Brain Sciences. 2021; 11(11):1461, Owaki D, Sekiguchi Y, Honda K, Izumi S.
  20. Classification of Ankle Joint Stiffness during Walking to Determine the Use of Ankle Foot Orthosis after Stroke, Brain Sciences. 2021; 11(11):1512, Sekiguchi Y, Honda K, Owaki D, Izumi S.

2021 学会発表 (Conference papers)

  1. Quantification of Joint Redundancy considering Dynamic Feasibility using Deep Reinforcement Learning, IEEE Int. Conf. on Robotics and Automation, June 2021, J. Chai, M. Hayashibe (ICRA2021)
  2. Deep Reinforcement Learning Framework for Underwater Locomotion of Soft Robot, IEEE Int. Conf. on Robotics and Automation, June 2021, L. Guanda, J. Shintake, M. Hayashibe (ICRA2021)
  3. Emergence of Motor Synergy in Multi-directional Reaching with Deep Reinforcement Learning, IEEE/SICE International Symposium on System Integration, Jan 2021, J. Han, J. Chai, M. Hayashibe (SII2021)
  4. Towards Robust Wheel-Legged Biped Robot System: Combining Feedforward and Feedback Control, IEEE/SICE International Symposium on System Integration, Jan 2021, F. Raza, M. Hayashibe (SII2021) (Finalist for Best Student Paper Award)
  5. Inter-Subject Transfer Learning Using Euclidean Alignment and Transfer Component Analysis for Motor Imagery-Based BCI, IEEE International Conference on Systems, Man, and Cybernetics, Oct 2021, O. Demsy, D. Achanccaray, M. Hayashibe (SMC2021)
  6. Mutual Information-Based Time Window Adaptation for Improving Motor Imagery-Based BCI, IEEE International Conference on Systems, Man, and Cybernetics, Oct 2021, C. Phunruangsakao, D. Achanccaray, M. Hayashibe (SMC2021)
  7. Seamless Temporal Gait Evaluation during Walking and Running Using Two IMU Sensors, 43rd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, Oct 2021, Y. Hutabarat, D. Owaki, M. Hayashibe (EMBC2021)
  8. Deep Reinforcement Learning with Gait Mode Specification for Quadrupedal Trot-Gallop Energetic Analysis, 43rd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, Oct 2021, J. Chai, D. Owaki, M. Hayashibe (EMBC2021)
  9. Simultaneous Quantification of Personalized Balance, Motion Class and Quality for Whole-body Exercise through Synergy Probe, 43rd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, Oct 2021, Felipe M. Ramos, M. Kojima, M. Hayashibe (EMBC2021)
  10. Whole Body Direction and Velocity Prediction from Leg Movements in Insect Walking Using Recurrent Neural Network, 32nd International Symposium on Micro-NanoMehatronics and Human Science, 2021, Y. Wang, M. Hayashibe, D. Owaki (Best Paper Award)
  11. 小嶋萌子, 大脇大, 林部充宏, “ゲーム環境下の重心角運動量に基づく不安定性推定による身体バランス機能の自動評価”, 日本機械学会東北支部 第56期総会・講演会, Mar 2021
  12. 杉山拓,沓澤 京、大脇 大,林部 充宏, “ニューラルネットワークによる水圧駆動型ソフトアクチュエータの汎化軌道追従制御”, 計測自動制御学会 東北支部 第331回研究集会, Mar 2021 (優秀発表奨励賞)
  13. 福西彬仁, 沓澤京, 大脇大, 林部充宏, “筋骨格モデルを用いた運動学習におけるモジュラリティの役割検証”, ロボティクス・メカトロニクス講演会(ROBOMECH2021), May 2021
  14. 吉田高志, Jiazheng Chai, 沓澤京, 大脇大, 林部充宏, “モデルベース強化学習により獲得される歩行運動に内在する脚協調構造”, ロボティクス・メカトロニクス講演会(ROBOMECH2021), May 2021
  15. 瀬宮優作, 沓澤京, 大脇大, 林部充宏, “エンコーダ-デコーダモデルによる脚ロボットの歩行動作生成”, ロボティクス・メカトロニクス講演会(ROBOMECH2021), May 2021
  16. 平井虎太朗, 沓澤京, 大脇大, 林部充宏, “モデルベース強化学習を用いたヘビ型ロボットの実験的検証”, ロボティクス・メカトロニクス講演会(ROBOMECH2021), May 2021
  17. 菅井諒, 大瀧亮二, 大内田裕, 林部充宏, 大脇大, “ウェアラブル表面筋電位計を用いた日常動作における両上肢の筋活動評価”, ロボティクス・メカトロニクス講演会(ROBOMECH2021), May 2021
  18. 納谷克海, 沓澤京, 大脇大, 林部充宏, “スパイキングニューラルネットワークに基づく深層強化学習による脚ロボットの歩行生成と評価”, 計測自動制御学会 東北支部 第332回研究集会, May 2021 (優秀発表奨励賞)
  19. 古関駿介, 沓澤京, 大脇大, 林部充宏, “深層強化学習を用いた準受動歩行および走行の実現”, 計測自動制御学会 東北支部 第334回研究集会, Sep 2021
  20. 小嶋萌子, 沓澤京, 大脇大, 林部充宏, “ゲーム環境下における重心情報を用いた運動機能の多角的評価”, 第22回計測自動制御学会SI部門講演会, Dec 2021
  21. 納谷克海沓澤京, 大脇大, 林部充宏, “スパイキングニューラルネットワークに基づく深層強化学習による脚ロボットの歩行生成と耐故障性評価”, 第22回計測自動制御学会SI部門講演会, Dec 2021
  22. 古畑和樹沓澤京, 大脇大, 林部充宏, “センサの移動を許容する複数深度カメラの人体骨格モデルの運動情報統合”, 第22回計測自動制御学会SI部門講演会, Dec 2021
  23. 前田真太郎, 関口雄介, 林部充宏, 大脇大, “深度カメラの運動学データを使用した再帰型ニューラルネットワークによる歩行時の床反力推定”, 第22回計測自動制御学会SI部門講演会, Dec 2021
  24. 高柳峻也沓澤京, 大脇大, 林部充宏, “時系列解析手法を用いた結合振動子系の同期ダイナミクス予測の評価”, 第22回計測自動制御学会SI部門講演会, Dec 2021

2020  学術雑誌・論文誌 (Journal papers)

  1. Motor Synergy Development in High-performing Deep Reinforcement Learning algorithms, IEEE Robotics and Automation Letters, April 2020, 5(2):1271-1278, Jiazheng Chai, M. Hayashibe, (ICRA2020 Paris) IF=3.6
  2. Discovering Interpretable Dynamics by Sparsity Promotion on Energy and the Lagrangian, IEEE Robotics and Automation Letters, April 2020, 5(2):2154-2160, Hoang K. Chu, M. Hayashibe, (ICRA2020 Paris) (Undergradate Student) IF=3.6
  3. Quantitative Gait Assessment with Feature-Rich Diversity Using Two IMU Sensors, IEEE Transactions on Medical Robotics and Bionics, vol.2, pp.639-648, (2020), (accepted), Y. Hutabarat, D. Owaki, M. Hayashibe
  4. Decoding Hand Motor Imagery Tasks within the Same Limb from EEG Signals Using Deep Learning, IEEE Transactions on Medical Robotics and Bionics, vol.2, pp.692-699, (2020), D. Achanccaray, M. Hayashibe
  5. Human-like Balance Recovery Based on Numerical Model Predictive Control Strategy, IEEE Access, vol.8, pp.92050 – 92060, (2020), K. Shen, A. Chemori, M. Hayashibe
  6. Reinforcement Q-Learning Control with Reward Shaping Function for Swing Phase Control in Semi Active Prosthetic Knee, Frontiers in Neurorobotics, 14:565702, (2020), Y. Hutabarat, K. Ekkachai, M. Hayashibe, W. Kongprawechnon
  7. Ankle–foot orthosis with dorsiflexion resistance using spring-cam mechanism increases knee flexion in the swing phase during walking in stroke patients with hemiplegia, Gait & Posture, Vol. 81, pp. 27-32, (2020), Y. Sekiguchi, D. Owaki, K. Honda, K. Fukushi, N. Hiroi, T. Nozaki, S. Izumi

2020 学会発表 (Conference papers)

  1. Simultaneous On-line Motion Discrimination and Evaluation of Whole-body Exercise by Synergy Probes for Home Rehabilitation, IEEE Int. Conf. on Robotics and Automation, June 2020, pp.10118-10124, Felipe M. Ramos, M. Hayashibe, (ICRA2020 Paris)
  2. Modeling and Control of a Hybrid Wheeled Legged Robot: Disturbance Analysis, IEEE/ASME International Conference on Advanced Intelligent Mechatronics, July 2020, pp.466-473, F. Raza, D. Owaki, M. Hayashibe, (AIM2020)
  3. Personalized Balance and Fall Risk Visualization with Kinect Two, 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, July 2020, pp.4863-4866, M. Hayashibe, A. Gonzalez, M. Tournier, (EMBC2020)
  4. J. Han, J. Chai, D. Owaki, M. Hayashibe, “Development of a Rimless Wheeled Robot That Enables Adaptive and Energy-efficient Locomotion”, ロボティクス・メカトロニクス講演会(ROBOMECH2020), May 2020
  5. M. Truong, S. Sakamoto, D. Owaki, M. Hayashibe, “EMG-based Estimation of Knee Torque and Angle using Recurrent Neural Network”, ロボティクス・メカトロニクス講演会(ROBOMECH2020), May 2020
  6. M.H. Ahmed, S. Shimoda, H. Hirata, M. Hayashibe, “Forearm Motion Estimation with Residual Shoulder Motion using Kinematic Synergies and Recurrent Neural Network”, ロボティクス・メカトロニクス講演会(ROBOMECH2020), May 2020
  7. Effectiveness Evaluation of Arm Usage for Human Quiet Standing Balance Recovery through Nonlinear Model Predictive Control, International Conference on Control and Robots, Dec 2020, K. Shen, A. Chemori, M. Hayashibe
  8. 坂本 誠一,大脇 大,林部 充宏, “再帰型ニューラルネットワークを用いた筋電位および慣性センサによる床反力推定”, 日本機械学会東北支部 第55期総会・講演会, Mar 2020
  9. 小嶋萌子, 大脇 大, 林部 充宏, “重心角運動量に基づく不安定性推定による身体バランス機能の自動評価”, ロボティクス・メカトロニクス講演会(ROBOMECH2020), May 2020
  10. 門山 尚貴,大脇 大,林部 充宏, “骨盤運動を有する準受動歩行の安定化に関する一考察”, 第38回日本ロボット学会学術講演会, Oct 2020
  11. 猪股 映史,Felipe M. Ramos,沓澤 京、大脇 大,林部 充宏, “サイクリング運動の速度・負荷変化に対する筋シナジー適応解析”, 第38回日本ロボット学会学術講演会, Oct 2020
  12. 杉山拓,沓澤 京、大脇 大,林部 充宏, “反復学習制御によるFiber-Reinforced Soft Actuatorの個体差補償”, 第38回日本ロボット学会学術講演会, Oct 2020
  13. 清水寛子,沓澤 京、大脇 大,林部 充宏, “深層強化学習を用いたばね付き準受動歩行モデルにおける歩容生成”, 第21回計測自動制御学会SI部門講演会, Dec 2020 (優秀講演賞受賞)

2019  学術雑誌・論文誌 (Journal papers)

  1. Identification of Time-Varying and Time-Scalable Synergies From Continuous Electromyographic Patterns, IEEE Robotics and Automation Letters, (2019), 4(3):3053-3058, Felipe M. Ramos, Andrea d’Avella, M. Hayashibe, (IROS2019 Macao) (国際共著論文 University of Messina, Italy) IF=3.6
  2. Augmenting Motor Imagery Learning for Brain-Computer Interfacing using Electrical Stimulation as Feedback, IEEE Transactions on Medical Robotics and Bionics, (2019), 1(4):247-255, S. Bhattacharyya, M. Clerc, M. Hayashibe, (accepted) (国際共著論文 INRIA, France and University of Essex, UK)
  3. Muscle Fatigue Induced Hand Tremor Clustering in Dynamic Laparoscopic Manipulation, IEEE Transactions on Systems, Man, and Cybernetics: Systems, (2019), S. Chandra, M. Hayashibe, A. Thondiyath, (国際共著論文 Indian Institute of Technology Madras, India and Rehabilitation Institute of Chicago, USA) IF=7.35
  4. Restoring Prolonged Standing via Functional Electrical Stimulation after Spinal Cord Injury: A Systematic Review of Control Strategies, Biomedical Signal Processing and Control, (2019), vol.49, pp.34-47 M.O. Ibitoye, N.A. Hamzaid, M. Hayashibe, N. Hasnan, and G.M. Davis (国際共著論文 University of Sydney, Australia)
  5. Centipede type robot i-centipot: From machine to creatures, Journal of Robotics and Mechatronics, Vol.31, 723-726, (2019), K. Osuka, T. Kinugasa, R. Hayashi, K. Yoshida, D. Owaki, A. Ishiguro
  6. 大脇大,脚を切られても歩きつづける昆虫とロボット,昆虫と自然,Vol. 54, pp. 31-34 (2019)
  7. 大脇大,動物の歩容を再現する四脚ロボット,日本ロボット学会誌,Vol.37, pp. 14-19, https://doi.org/10.7210/jrsj.37.126,  (2019)

2019 学会発表 (Conference papers)

  1. D. Achanccaray, J.M. Chau, J. Pirca, F. Sepulveda, M. Hayashibe, “Assistive Robot Arm Controlled by a P300-based Brain Machine Interface for Daily Activities”, 9th Int. IEEE EMBS Neural Engineering Conf., USA, 2019
  2. S. Takayanagi, D. Owaki, M. Hayashibe, “Online Prediction of the Synchronization Dynamics generated by Coupled Oscillator System”, in Proc. of 9th Int. Symposium on Adaptive Motion of Animals and Mechanics, Lausanne, August 2019
  3. R. Zamboni, D. Owaki, M. Hayashibe, “Energy Efficiency Analysis of the Tegotae Approach for Bio-inspired Hopping”, in Proc. of 9th Int. Symposium on Adaptive Motion of Animals and Mechanics, Lausanne, August 2019
  4. D. Owaki, V. Duerr, J. Schmitz, “Motion Hacking -Toward Control of Insect Walking-”, in Proc. of 9th Int. Symposium on Adaptive Motion of Animals and Mechanics, Lausanne, August 2019
  5. W. Lu, D. Owaki, M. Hayashibe, “Textile-based Electrode Array for FES and sEMG Recording Fabricated by Screen Printing”, in Proc. of 30th 2019 International Symposium on Micro-NanoMehatronics and Human Science, 2019
  6. 高柳 峻也,大脇 大,林部 充宏, “結合振動子系を模したメトロノーム群の同期ダイナミクス予測”, 計測自動制御学会 東北支部 第321回研究集会, Feb 2019
  7. 山川 友希,大脇 大,林部 充宏, “再帰型ニューラルネットワークを用いた手指運動の判別と評価”, 計測自動制御学会 東北支部 第322回研究集会, May 2019
  8. 高柳 峻也,大脇 大,林部 充宏, “実世界における結合振動子系同期ダイナミクスのオンライン予測”, ロボティクス・メカトロニクス講, Jun 2019
  9. 坂本 誠一,大脇 大,林部 充宏, “再帰型ニューラルネットワークを用いた筋電位による床反力推定”, ロボティクス・メカトロニクス講演会, Jun 2019
  10. 山川 友希,大脇 大,林部 充宏, “再帰型ニューラルネットワークを用いた手指動作の自動判別と評価”, 第37回日本ロボット学会学術講演会, Sep 2019
  11. 門山 尚貴,大脇 大,林部 充宏, “腰運動へのエネルギ―補填を介した準受動歩行の検討”, 第37回日本ロボット学会学術講演会, Sep 2019
  12. 浜田 淳司,Jiazheng Chai,大脇 大,林部 充宏, “フィードバック誤差学習から着想を得た力制御手法におけるエネルギ効率の評価”, 第37回日本ロボット学会学術講演会, Sep 2019
  13. 猪股 映史,Felipe M. Ramos,大脇 大,林部 充宏, “サイクリング速度による筋シナジー遷移解析”, 計測自動制御学会 東北支部 第327回研究集会, Dec 2019

2018  学術雑誌・論文誌 (Journal papers)

  1. Synergetic Learning Control Paradigm for Redundant Robot to Enhance Error-Energy Index, IEEE Transactions on Cognitive and Developmental Systems, (2018), 10(3):573-584, M. Hayashibe, S. Shimoda
  2. Real-time closed-loop functional electrical stimulation control of muscle activation with evoked electromyography feedback for spinal cord injured patients, International Journal of Neural Systems, (2018), 28(6):1750063, Z. Li, D. Guiraud, D. Andreu, A. Gelis, C. Fattal, and M. Hayashibe (国際共著論文 INRIA, University of Montpellier, France)IF=6.4
  3. Virtual Reality based Center of Mass Assisted Personalized Balance Training System, Front. Bioeng. Biotechnol., (2018), 5:85, D. Kumar, A. Gonzalez, A. Das, A. Dutta, P. Fraisse, M. Hayashibe and U. Lahiri(国際共著論文 Indian Institute of Technology Gandhinagar, India)IF=5.1
  4. Generation of Human-like Movement from Symbolized Information, Frontiers in Neurorobotics, (2018), 12:43, S. Okajima, M. Tournier, F. S Alnajjar, M. Hayashibe, Y. Hasegawa, S. Shimoda
  5. Regulation of quasi-joint stiffness by combination of activation of ankle muscles in midstances during gait in patients with hemiparesis, Gait & Posture, (2018), vol. 62, pp. 378-383, Y. Sekiguchi, T. Muraki, D. Owaki, K. Honda, S. Izumi
  6. Spontaneous gait transition to high-speed galloping by reconciliation between body support and propulsion, Advanced Robotics, (2018), doi:10.1080/01691864.2018.1501277, A. Fukuhara, D. Owaki, T. Kano, R. Kobayashi, A. Ishiguro

2018 学会発表 (Conference papers)

  1. D. Achanccaray, K. Pacheco, E. Carranza, M. Hayashibe, “Immersive Virtual Reality Feedback in a Brain Computer Interface for Upper Limb Rehabilitation”, The 2018 IEEE International Conference on Systems, Man, and Cybernetics, Miyazaki, Japan, Oct 2018
  2. D. Achanccaray, J. Astucuri, M. Hayashibe, J. Pirca, and V. Espinoza, “Implication of N400 and P600 waves in the Linguistic Code Change in Monolinguals and Bilinguals”, International Conference of the IEEE Engineering in Medicine and Biology Society, Hawaii, July 2018
  3. D. Owaki, Y. Sugimoto, A. Ishiguro, and H. Aonuma, “Change in Electromyographic Patterns After Leg Amputation in the Cricket”, ICN2018 International Congress of Neuroethology, (2018).
  4. 大脇大,杉本靖博,石黒章夫,青沼仁志,”コオロギの脚切断後の筋電位パターンの変容”,第30回自律分散システム・シンポジウム資料,2A2-4 (2018).

2017  学術雑誌・論文誌 (Journal papers)

  1. Automatic Human Movement Assessment with Switching Linear Dynamic System: Motion Segmentation and Motor Performance, IEEE Transactions on Neural Systems & Rehabilitation Engineering, (2017), 25(6):628-640, Roberto de Souza Baptista, Antonio P. L. Bo, Mitsuhiro Hayashibe (査読有) IF=3.9
  2. A Generic Transferable EEG Decoder for Online Detection of Error Potential in Target Selection, Frontiers in Neuroscience, (2017), 11:226, S. Bhattacharyya, A. Konar, D.N. Tibarewala, M. Hayashibe (査読有) IF=3.6
  3. M. Hayashibe, A. Gonzalez, P. Fraisse, “Personalized Modeling for Home-based Balance Rehabilitation”, Elsevier, Book chapter In Human Modelling for Bio-inspired Robotics, edited by Jun Ueda and Yuichi Kurita, Academic Press, 2017, Pages 111-137, ISBN 9780128031377, http://dx.doi.org/10.1016/B978-0-12-803137-7.00005-7
  4. A Minimal Model Describing Hexapedal Interlimb Coordination: the Tegotae-based Approach, Frontiers in Neurorobotics, vol. 11, 29, (2017), D. Owaki, M. Goda, S. Miyazawa, A. Ishiguro (査読有)
  5. Decentralized control mechanism underlying interlimb coordination of millipedes”, Bioinspiration & Biomimetics, 12, 036007, doi: 10.1088/1748-3190/aa64a5, (2017), T. Kano, K. Sakai, K. Yasui, D. Owaki, A. Ishiguro (査読有)
  6. A Quadruped Robot Exhibiting Spontaneous Gait Transitions from Walking to Trotting to Galloping”, Scientific Reports, 7, 277, doi:  10.1038/s41598-017-00348-9, (2017), D. Owaki and A. Ishiguro (査読有) IF2016=4.3
  7. Decentralized control scheme for myriapod robot inspired by adaptive and resilient centipede locomotion”, PLOS ONE, 12, e0171421, doi: 10.1038/s41598-017-00348-9, (2017), K. Yasui, K. Sakai, T. Kano, D. Owaki, A. Ishiguro (査読有)
  8. Cycling with Spinal Cord Injury: A Novel System for Cycling Using Electrical Stimulation for Individuals with Paraplegia, and Preparation for Cybathlon 2016, IEEE Robotics & Automation Magazine, Vol. 24, 4, pp.58-65, (2017), Antônio P.L. Bó, Lucas O. da Fonseca, Juliana A. Guimarães, Emerson Fachin-Martins, Miguel E.G. Paredes, George A. Brindeiro, Ana Carolina C. de Sousa, Marien C.N. Dorado, and Felipe Ramos IF=4.25
  9. 大脇大,”脚式ロコモーションに内在する制御メカニズムの解明を目指して”,日本神経回路学会誌,Vol. 24, No. 4, pp. 162-171, https://doi.org/10.3902/jnns.24.162 (2017).

2017 学会発表 (Conference papers)

  1. M. Hayashibe, S. Shimoda, and A. Ijspeert, “Synergetic Learning Control Paradigm for Forming Adaptive Central Pattern Generators”, in Proc. of 8th International Symposium on Adaptive Motion of Animals and Mechanics, Sapporo, June 2017
  2. D. Owaki, Y. Sugimoto, A. Ishiguro, and H. Aonuma, “Change in coordinated motor patterns after leg amputation in the cricket”, JSCPB2017, Fukuoka (2017)
  3. 関口雄介,大脇大, 本田啓太 ,広井典良 ,福司謙一郎 ,野崎岳夫,出江紳一,弾性調整型股関節装具と足の組み合わせが 脳卒中片麻痺患者の歩行に及ぼす影響,第38回バイオメカニズム学術講演会,別府(大分),(2017).
  4. 本田啓太、関口雄介、大脇大、出江紳一,足底圧感覚の聴覚フィードバック装具が脳卒中片麻痺患者の歩行安定性に及ぼす影響,第38回バイオメカニズム学術講演会,別府(大分),(2017).
  5. 関口雄介,大脇大,本田啓太,広井典良,福司謙一郎,野崎岳夫,出江紳一, 弾性股関節装具が脳卒中片麻痺患者の歩行パフォーマンスに及ぼす効果について,第33回日本義肢装具学会学術大会,東京,(2017).
  6. 関口雄介,大脇大,本田啓太,野崎岳夫,福司謙一郎,広井典良,出江紳一,カム-バネ継手付きゲイトソリューションが脳卒中片麻痺患者の歩行パフォーマンスに及ぼす効果について,第33回日本義肢装具学会学術大会,東京,(2017).

2016  学術雑誌・論文誌 (Journal papers)

  1. A Synergetic Brain-machine Interfacing Paradigm for Multi-DOF Robot Control, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46, 7, (2016), 957-968. S. Bhattacharyya, S. Shimoda and M Hayashibe (査読有) IF=5.1
  2. Evoked Electromyographically Controlled Electrical Stimulation, Frontiers in Neuroscience, 10, 335, (2016). M. Hayashibe (査読有) IF=3.6
  3. NIRS-EEG joint imaging during transcranial direct current stimulation: online parameter estimation with an autoregressive model, Journal of Neuroscience Methods, (2016), (doi:10.1016/j.jneumeth.2016.09.008), M. Sood, P. Besson, M. Muthalib, U. Jindal, S. Perrey, A. Dutta, M. Hayashibe (査読有)
  4. Real-time estimation of FES-induced joint torque with evoked EMG: Application to spinal cord injured patients, Journal of NeuroEngineering and Rehabilitation, 13, (2016), Article 60, Zhan Li, David Guiraud, David Andreu, Mourad Benoussaad, Charles Fattal and M. Hayashibe (査読有) IF=4.6
  5. Empirical Mode Decomposition-Based Filtering for Fatigue Induced Hand Tremor in Laparoscopic Manipulation, Biomedical Signal Processing and Control, vol.31, pp.339-349, (2016), S. Chandra, M. Hayashibe, A. Thondiyath (査読有)
  6. Differential Analysis of Muscle Fatigue Induced Elbow and Wrist Tremor in Controlled Laparoscopic Manoeuvring, International Journal of Medical Robotics and Computer Assisted Surgery, Article 1772, (2016), S. Chandra, M. Hayashibe, A. Thondiyath, M. Ramalingam (査読有)
  7. Short-Term Effect of Prosthesis Transforming Sensory Modalities on Walking in Stroke Patients with Hemiparesis”, Neural Plasticity, 6809879, doi: 10.1155/2016/6809879, (2016), D. Owaki, Y. Sekiguchi, K. Honda, A. Ishiguro, S. Izumi (査読有)

2015 学術雑誌・論文誌 (Journal papers)

  1. The difference between electrical microstimulation and direct electrical stimulation – towards new opportunities for innovative functional brain mapping?, Reviews in the Neurosciences, 27, 3, (2015), 231-258. M. Vincent, O. Rossel, M. Hayashibe, G. Herbet, H. Duffau, D. Guiraud, F. Bonnetblanc (査読有)
  2. Case report: Remote neuromodulation with direct electrical stimulation of the brain, as evidenced by intra-operative EEG recordings during wide-awake neurosurgery, Clinical Neurophysiology, 127, 2, (2015), 1752-4. M. Vincent, O. Rossel, B. Poulin-Charronnat, G. Herbet, M. Hayashibe, H. Duffau, D. Guiraud, F. Bonnetblanc (査読有) IF=3.7
  3. Synthesis of Optimal Electrical Stimulation Patterns for Functional Motion Restoration Applied to Spinal Cord Injured Patients, Medical & Biological Engineering & Computing, 53, (2015), 227-240. M. Benoussaad, P. Poignet, M. Hayashibe, C. Azevedo-Coste, C. Fattal, D. Guiraud (査読有)
  4. Inverse Estimation of Multiple Muscle Activations from Joint Moment with Muscle Synergy Extraction, IEEE Journal of Biomedical and Health Informatics, 19, 1, (2015), 64-73. Z. Li, D. Guiraud, M. Hayashibe (査読有) IF=4.2
  5. Adaptive Interface for Personalized Center of Mass Self-identification in Home Rehabilitation, IEEE Sensors Journal, 15, 5, (2015), 2814-2823. A. Gonzalez, P. Fraisse, M. Hayashibe (査読有)
  6. Human Movement Understanding (TC spotlight), IEEE Robotics and Automation Magazine, 22, 3, (2015), 22-24. E. Demircan, D. Kulic, D. Oetomo, M. Hayashibe (査読有) IF=4.25
  7. Biosignal processing and computational methods to enhance sensory motor neuroprosthetics, Frontiers in Neuroscience, 9, 434, (2015). M. Hayashibe, D. Guiraud, J. Pons, D. Farina (査読有) IF=3.6
  8. Determination of subject specific whole-body centre of mass using the 3D Statically Equivalent Serial Chain, Gait & Posture, vol.41, no.1, pp.70-5, 2015. (doi:10.1016/j.gaitpost.2014.08.017). V. Bonnet, A. Gonzales, C. Azevedo-Coste, M. Hayashibe, S. Cotton, P. Fraisse (査読有)
  9. Empirical Mode Analysis for Characterization of Hand Tremor in the Design of Laparoscopic Tools, ASME. J. Med. Devices, vol.9, no.3, 030932-030932-3, 2015. (doi:10.1115/1.4030563). S. Chandra, M. Hayashibe, A. Thondiyath (査読有)
  10. Hereditary sensory and autonomic neuropathy types 4 and 5: review and proposal of a new rehabilitation method, Neuroscience Research, vol. 105, pp. 105-111 (2015). Arito Yozu, Nobuhiko Haga, Tetsuro Funato, Dai Owaki, Ryosuke Chiba, and Jun Ota (査読有)

2014 学術雑誌・論文誌 (Journal papers)

  1. A new method for muscle fatigue assessment: Online model identification techniques, Muscle & Nerve, 50, (2014), 556-563. M. Papaiordanidou, M. Hayashibe, A. Varray, C. Fattal, D. Guiraud (査読有)
  2. Asymmetric interhemispheric excitability evidenced by event-related potential amplitude patterns after “wide-awake surgery” of brain tumours, Experimental Brain Research, 232, 12, (2014), 3907-3918. F. Bonnetblanc, G. Herbet, P. Charras, M. Hayashibe, D. Guiraud, H. Duffau, B. Poulin-Charronnat (査読有)
  3. Muscle Fatigue Tracking with Evoked EMG via Recurrent Neural Network: Toward Personalized Neuroprosthetics, IEEE Computational Intelligence Magazine, 9, 2, (2014), 38-46. Z. Li, M. Hayashibe, C. Fattal, D. Guiraud (査読有) IF=5.8
  4. Whole Body Center of Mass Estimation with Portable Sensors: Using the Statically Equivalent Serial Chain and a Kinect, Sensors, vol.14, no.9, (2014), 16955-16971. A. Gonzalez, M. Hayashibe, V. Bonnet, P. Fraisse (査読有)
  5. Synergetic Motor Control Paradigm for Optimizing Energy Efficiency of Multijoint Reaching via Tacit Learning, Frontiers in Computational Neuroscience, 8, 21, (2014). M. Hayashibe, S. Shimoda (査読有)

2013 学術雑誌・論文誌 (Journal papers)

  1. Evoked Electromyography-Based Closed-Loop Torque Control in Functional Electrical Stimulation, IEEE Transactions on Biomedical Engineering, vol.60, no.8, (2013), 2299-2307. Q. Zhang, M. Hayashibe, C. Azevedo-Coste (査読有) IF=4.5
  2. Voluntary EMG-to-Force Estimation with a Multi-Scale Physiological Muscle Model, Biomedical Engineering Online, vol.12, no.86, doi:10.1186/1475-925X-12-86, (2013). M. Hayashibe, D. Guiraud (査読有)
  3. In-vivo Identification of Skeletal Muscle Dynamics with Nonlinear Kalman Filter: Comparison between EKF and SPKF, ISRN Rehabilitation, vol.2013, 610709, 10 pages, (2013), M. Hayashibe, D. Guiraud, P. Poignet (査読有)
  4. Experimental parameter identification of a multi-scale musculoskeletal model controlled by electrical stimulation: application to patients with spinal cord injury, Medical & Biological Engineering & Computing, vol.51, (2013), 617-631. M. Benoussaad, P. Poignet, M. Hayashibe, C. Azevedo-Coste, C. Fattal, D. Guiraud (査読有)

2011 学術雑誌・論文誌 (Journal papers)

  1. Hayashibe, Q. Zhang, D. Guiraud, C. Fattal, “Evoked EMG based Torque Prediction under Muscle Fatigue in Implanted Neural Stimulation”, Journal of Neural Engineering, vol.8, 064001,2011. (doi:10.1088/1741-2560/8/6/064001) IF=4.5
  2. Q. Zhang, M. Hayashibe, D. Guiraud, P. Fraisse, “FES-Induced Torque Prediction with Evoked EMG Sensing for Muscle Fatigue Tracking”, IEEE/ASME Transactions on Mechatronics, Focused Section on Biosignal sensing, vol.16, no.5, pp.816-826, 2011. IF=4.9