Rehabilitation Support System Using Multimodal Interface Device for Aphasia Patients

  • Takuya Mabuchi
  • Joji Sato
  • Takahiro Takeda
  • Naoyuki Kubota
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9835)


In this research, develops a novel system using Multimodal Interface Device for supporting the rehabilitation of aphasia. The system supports diagnosis by conducting statistical analysis of the diagnostic result. So, it’s necessary to share a check result by uploading large-volume data in a data server. It is possible to simply perform comparison between data sets and upload patient’s data into a database based on previous statistics.


Aphasia Higher brain function disorder Computational system rehabilitation Rehabilitation support system 


  1. 1.
    Rehabilitation following acquired brain injury, Royal College of Physicians, 23 July.
  2. 2.
    Johansson, M.B.: Aphasia and Communication in Everyday Life: Experiences of persons with aphasia, significant others, and speech-language pathologists. Doctoral thesis of Uppsala University (2012)Google Scholar
  3. 3.
    Obo, T., Kusaka, J., Kubota, N., Nitta, O., Matsuda, T.: Informationally structured space for computational system rehabilitation. In: Proceedings of 2014 IEEE International Conference on System, Man, and Cybernetics (2014)Google Scholar
  4. 4.
    Japan Society for Higher Brain Dysfunction: Standard Language Test of Aphasia Manual, 2nd edn. 6th impression. Shinko Medical Publisher (2013)Google Scholar
  5. 5.
    Kubota, N., Sotobayashi, H., Obo, T.: Human interaction and behavior understanding based on sensor network with iPhone for rehabilitation. In: Proceedings of the International Workshop on Advanced Computational Intelligence and Intelligent Informatics (2009)Google Scholar
  6. 6.
    Kubota, N., Yorita, A.: Topological environment reconstruction in informationally structured space for pocket robot partners. In: Proceedings of the 2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA 2009), Daejeon, Korea, pp. 165–170 (2009)Google Scholar
  7. 7.
    Kubota, N., Botzheim, J., Obo, T.: Human motion tracking and feature extraction for cognitive rehabilitation in informationally structured space. In: Proceedings of the 9th France-Japan and 7th Europe-Asia Congress on Mechatronics and the 13th International Workshop on Research and Education in Mechatronics (REM), Paris, France, pp. 464–471 (2012)Google Scholar
  8. 8.
    Botzheim, J., Obo, T., Kubota, N.: Human gesture recognition for robot partners by spiking neural network and classification learning. In: Proceedings of Joint 6th International Conference on Soft Computing and Intelligent Systems and 13th International Symposium on Advanced Intelligent Systems (SCIS & ISIS 2012), Kobe, Japan, pp. 1954–1958 (2012)Google Scholar
  9. 9.
    Hiwada, E., Aoki, O., Kubota, N.: Appearance and motion pattern generator of balls in a support system for diagnosis of unilateral spatial neglect. In: Proceedings of the 21st Fuzzy, Artificial Intelligence, Neural Networks and Computational Intelligence, pp. 47–50 (2011) (in Japanese)Google Scholar
  10. 10.
    Browndyke, J.N.: Aphasia Assessment. Neuropsychology Central, pp. 1–7 (2002)Google Scholar
  11. 11.
    Lezak, M.: Neuropsychological Assessment. Oxford University, New York (2012)Google Scholar
  12. 12.
    Mazec, MetaMoji Corporation.

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Takuya Mabuchi
    • 1
  • Joji Sato
    • 1
  • Takahiro Takeda
    • 1
  • Naoyuki Kubota
    • 1
  1. 1.Graduate School of System DesignTokyo Metropolitan UniversityHinoJapan

Personalised recommendations