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Assistant Professor

Laboratory location

Sugimoto Campus


Asuka Takai is, under the cross-appointment system, also belonging to the Department of Brain Robot Interface, Advanced Telecommunications Research Institute (ATR).

Degree 【 display / non-display

  • Osaka Prefecture University (Japan) -  Doctor of Engineering

  • Osaka Prefecture University (Japan) -  Master of Engineering

  • King's College London (UK) -  Master of Science in Robotics

  • Osaka Prefecture University (Japan) -  Bachelor of Engineering

Research Areas 【 display / non-display

Rehabilitation science/Welfare engineering, Human interface and interaction, Dynamics/Control, Intelligent robotics, Intelligent mechanics/Mechanical systems

Research subject summary 【 display / non-display

  • Understanding the underlying mechanism and its functional roles of human movement and applying the findings to develop motor learning support systems and robotic rehabilitation systems.

Research Interests 【 display / non-display

Analysis of human movement, Human movement assistive engineering, Musculoskeletal rehabilitation, Human-human and human-robot interaction

Research Career 【 display / non-display

  • Study on Individualized Assistance during the Sit-to-Stand Movement Capable of Reducing Body Loads and Utilizing Residual Functions.

    (Individual) Project Year :


  • Development of exoskeletal robot as for rehabilitation system to reconstruct upper and lower limb motor functions

    (Collaboration in Japan) Project Year :


  • Research on changes in brain activity related to motor learning and improvement of motor skills using brain robot interface technology

    (Collaboration in Organization) Project Year :


Current Career 【 display / non-display

  • Osaka City University   Graduate School of Engineering   Mechanical and Physical Engineering Course   Assistant Professor  


Published Papers 【 display / non-display

  • Investigation on the Neural Correlates of Haptic Training.

    Asuka Takai, Diletta Rivela, Giuseppe Lisi, Tomoyuki Noda, Tatsuya Teramae, Hiroshi Imamizu, Jun Morimoto

    IEEE IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Miyazaki, Japan, October 7-10, 2018    519 - 523 2019.01  [Refereed]


  • Markov Switching Model for Quick Detection of Event Related Desynchronization in EEG

    Lisi Giuseppe, Rivela Diletta, Takai Asuka, Morimoto Jun

    FRONTIERS IN NEUROSCIENCE  12 ( FEB ) 24 - 24 2018.02  [Refereed]

    DOI PubMed

  • Robotizing Double-Bar Ankle-Foot Orthosis.

    Tomoyuki Noda, Asuka Takai, Tatsuya Teramae, Eiko Hirookai, Kimitaka Hase, Jun Morimoto

    IEEE COMPUTER SOC Proceedings - IEEE International Conference on Robotics and Automation    2782 - 2787 2018  [Refereed]

     View Summary

    © 2018 IEEE. This paper introduces an approach that robotizes an ankle-foot orthosis (AFO). In particular, toward post-stroke gait rehabilitation, we robotize a double-bar AFO, which is widely used in rehabilitation facilities, by newly designing a modular joint, a pneumatic actuator, and a Bowden cable force-transmission system. Our modular joint system, called the Modular Exoskeletal Joint (MEJ), has a hollow shaft for simple attachment to an AFO's pivot. We designed MEJ to compactly house an encoder that is built in a bearing in a pulley. We adopted Bowden cables to transmit contraction forces from an actuator to the MEJ. As an actuation scheme, we developed the Nested-cylinder Pneumatic Artificial Muscle (NcPAM) system. Even though PAMs are mechanically compliant and lightweight, they can still generate a large force. Therefore, they can provide an ideal actuation system for exoskeletal robots. The nested-cylinder in NcPAM houses a cable-tensioning spring to properly maintain small cable tension for passive movements and a cable stopper to connect the PAM and the cable for properly transmitting the large force generated by PAM. We show the ankle-joint trajectory tracking performances of this integrated system using iterative learning control.


  • Database-driven approach for Biosignal-based robot control with collaborative filtering.

    Jun-ichiro Furukawa, Asuka Takai, Jun Morimoto

    IEEE 17th IEEE-RAS International Conference on Humanoid Robotics, Humanoids 2017, Birmingham, United Kingdom, November 15-17, 2017    606 - 611 2017.12  [Refereed]


  • Minimization of body loads during sit-to-stand movement by continuous genetic algorithm

    TAKAI Asuka, NAKAGAWA Chihiro, SHINTANI Atsuhiko, ITO Tomohiro

    The Japan Society of Mechanical Engineers, Transactions of the JSME (in Japanese)  80 ( 812 ) TRANS0064 - TRANS0064 2014  [Refereed]

     View Summary

    Sit-to-stand (STS) rehabilitation equipment traditionally simulates natural movements of unspecified healthy people. This type of movement may not place the lowest load on a patient's body. These factors let us develop a novel and widely applicable system for rehabilitation purposes that suggests a motion that places low body loads at the lower limb joints. This paper describes the core computation in the system that calculates a STS movement that places a minimum body load by using Continuous Genetic Algorithm (CGA). The minimization process starts from measurement of kinematic and kinetic data of a human subject by using gyroscopic sensors and reaction force plates, and then estimation and evaluation of the body load are followed. The body load which is minimized during STS movement was quantified by an index value. The index value strongly correlates with the chair height. Decreasing this index value by changing the movement during STS transfer could reduce the impact on the body, which is the same amount of load placed on the body while standing up from a higher chair height. The angular displacements of the averaged natural STS movement of the subject were considered as one of the several ways to stand up and the movements were assessed by means of the index value in CGA. A modified STS movement that can minimize the body load was computed after the optimization and it could be a personalized optimum movement allowing users to conduct rehabilitation with lesser unnecessary body load.


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Review Papers (Misc) 【 display / non-display

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Conference Activities & Talks 【 display / non-display

  • Neural investigation towards motor skill improvements through brain-computer interface-based training

    Takai A, Rivela D, Lisi G, Noda T, Teramae T, Imamizu H, Morimoto J

    Brain-Computer Interface Samara 2020 and Samara NeuroWeek Proceedings pp. 23-25  2020.10