A multichannel-near-infrared-spectroscopy-triggered robotic hand rehabilitation system for stroke patients

Jongseung Lee, Nobutaka Mukae, Jumpei Arata, Hiroyuki Iwata, Keiji Iramina, Koji Iihara, Makoto Hashizume

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

There is a demand for a new neurorehabilitation modality with a brain-computer interface for stroke patients with insufficient or no remaining hand motor function. We previously developed a robotic hand rehabilitation system triggered by multichannel near-infrared spectroscopy (NIRS) to address this demand. In a preliminary prototype system, a robotic hand orthosis, providing one degree-of-freedom motion for a hand's closing and opening, is triggered by a wireless command from a NIRS system, capturing a subject's motor cortex activation. To examine the feasibility of the prototype, we conducted a preliminary test involving six neurologically intact participants. The test comprised a series of evaluations for two aspects of neurorehabilitation training in a real-time manner: classification accuracy and execution time. The effects of classification-related factors, namely the algorithm, signal type, and number of NIRS channels, were investigated. In the comparison of algorithms, linear discrimination analysis performed better than the support vector machine in terms of both accuracy and training time. The oxyhemoglobin versus deoxyhemoglobin comparison revealed that the two concentrations almost equally contribute to the hand motion estimation. The relationship between the number of NIRS channels and accuracy indicated that a certain number of channels are needed and suggested a need for a method of selecting informative channels. The computation time of 5.84 ms was acceptable for our purpose. Overall, the preliminary prototype showed sufficient feasibility for further development and clinical testing with stroke patients.

Original languageEnglish
Title of host publication2017 International Conference on Rehabilitation Robotics, ICORR 2017
EditorsArash Ajoudani, Panagiotis Artemiadis, Philipp Beckerle, Giorgio Grioli, Olivier Lambercy, Katja Mombaur, Domen Novak, Georg Rauter, Carlos Rodriguez Guerrero, Gionata Salvietti, Farshid Amirabdollahian, Sivakumar Balasubramanian, Claudio Castellini, Giovanni Di Pino, Zhao Guo, Charmayne Hughes, Fumiya Iida, Tommaso Lenzi, Emanuele Ruffaldi, Fabrizio Sergi, Gim Song Soh, Marco Caimmi, Leonardo Cappello, Raffaella Carloni, Tom Carlson, Maura Casadio, Martina Coscia, Dalia De Santis, Arturo Forner-Cordero, Matthew Howard, Davide Piovesan, Adriano Siqueira, Frank Sup, Masia Lorenzo, Manuel Giuseppe Catalano, Hyunglae Lee, Carlo Menon, Stanisa Raspopovic, Mo Rastgaar, Renaud Ronsse, Edwin van Asseldonk, Bram Vanderborght, Madhusudhan Venkadesan, Matteo Bianchi, David Braun, Sasha Blue Godfrey, Fulvio Mastrogiovanni, Andrew McDaid, Stefano Rossi, Jacopo Zenzeri, Domenico Formica, Nikolaos Karavas, Laura Marchal-Crespo, Kyle B. Reed, Nevio Luigi Tagliamonte, Etienne Burdet, Angelo Basteris, Domenico Campolo, Ashish Deshpande, Venketesh Dubey, Asif Hussain, Vittorio Sanguineti, Ramazan Unal, Glauco Augusto de Paula Caurin, Yasuharu Koike, Stefano Mazzoleni, Hyung-Soon Park, C. David Remy, Ludovic Saint-Bauzel, Nikos Tsagarakis, Jan Veneman, Wenlong Zhang
PublisherIEEE Computer Society
Pages158-163
Number of pages6
ISBN (Electronic)9781538622964
DOIs
Publication statusPublished - Aug 11 2017
Event2017 International Conference on Rehabilitation Robotics, ICORR 2017 - London, United Kingdom
Duration: Jul 17 2017Jul 20 2017

Publication series

NameIEEE International Conference on Rehabilitation Robotics
ISSN (Print)1945-7898
ISSN (Electronic)1945-7901

Other

Other2017 International Conference on Rehabilitation Robotics, ICORR 2017
CountryUnited Kingdom
CityLondon
Period7/17/177/20/17

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Rehabilitation
  • Electrical and Electronic Engineering

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  • Cite this

    Lee, J., Mukae, N., Arata, J., Iwata, H., Iramina, K., Iihara, K., & Hashizume, M. (2017). A multichannel-near-infrared-spectroscopy-triggered robotic hand rehabilitation system for stroke patients. In A. Ajoudani, P. Artemiadis, P. Beckerle, G. Grioli, O. Lambercy, K. Mombaur, D. Novak, G. Rauter, C. Rodriguez Guerrero, G. Salvietti, F. Amirabdollahian, S. Balasubramanian, C. Castellini, G. Di Pino, Z. Guo, C. Hughes, F. Iida, T. Lenzi, E. Ruffaldi, F. Sergi, G. S. Soh, M. Caimmi, L. Cappello, R. Carloni, T. Carlson, M. Casadio, M. Coscia, D. De Santis, A. Forner-Cordero, M. Howard, D. Piovesan, A. Siqueira, F. Sup, M. Lorenzo, M. G. Catalano, H. Lee, C. Menon, S. Raspopovic, M. Rastgaar, R. Ronsse, E. van Asseldonk, B. Vanderborght, M. Venkadesan, M. Bianchi, D. Braun, S. B. Godfrey, F. Mastrogiovanni, A. McDaid, S. Rossi, J. Zenzeri, D. Formica, N. Karavas, L. Marchal-Crespo, K. B. Reed, N. L. Tagliamonte, E. Burdet, A. Basteris, D. Campolo, A. Deshpande, V. Dubey, A. Hussain, V. Sanguineti, R. Unal, G. A. D. P. Caurin, Y. Koike, S. Mazzoleni, H-S. Park, C. D. Remy, L. Saint-Bauzel, N. Tsagarakis, J. Veneman, ... W. Zhang (Eds.), 2017 International Conference on Rehabilitation Robotics, ICORR 2017 (pp. 158-163). [8009239] (IEEE International Conference on Rehabilitation Robotics). IEEE Computer Society. https://doi.org/10.1109/ICORR.2017.8009239