Walking speed intention model using soleus electromyogram signal of nondisabled and post-stroke hemiparetic patients

Sang Hun Chung, Taejin Choi, Yoha Hwang, Hyungmin Kim, Seung-Jong Kim, Min Ho Chun, Jong Min Lee

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

2 Citations (Scopus)

Abstract

It is well known that the activation of plantar flexors have a strong influence on the walking speed. If the gait speed can be predicted using this relationship, a post-stroke hemiparetic patient could control a gait rehabilitation robot according to his or her gait intention, and the robotic gait rehabilitation effect could be further improved. To find out this relationship, 9 nondisabled subjects and 4 chronic post-stroke hemiparetic subjects performed overground level walking at a comfortable pace, a slow pace, a fast pace, and an increasing pace with electromyogram sensors attached on plantar flexors. Soleus among plantar flexors showed the most stable relationship with walking speed. The relationship between maximum activation level of soleus electromyogram during stance phase before toe-off and walking speed during swing phase after the same toe-off was modeled by a polynomial regression model. The model outputs were then compared to the measured walking speeds using coefficients of determination (R2). The average R2 values are 0.594 and 0.692 for 1st· and 2nd order models respectively in the nondisabled subjects. The average R2 values are 0.598 and 0.623 for the unaffected side and 0.388 and 0.394 for the affected side in the chronic subjects. The results show the feasibility of applying the soleus-walking speed relationship to control the robot gait speed at will. A walking speed estimation method is proposed using only a walking step in real time.

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
Pages308-313
Number of pages6
ISBN (Electronic)9781538622964
DOIs
Publication statusPublished - 2017 Aug 11
Externally publishedYes
Event2017 International Conference on Rehabilitation Robotics, ICORR 2017 - London, United Kingdom
Duration: 2017 Jul 172017 Jul 20

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
Period17/7/1717/7/20

ASJC Scopus subject areas

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

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    Chung, S. H., Choi, T., Hwang, Y., Kim, H., Kim, S-J., Chun, M. H., & Lee, J. M. (2017). Walking speed intention model using soleus electromyogram signal of nondisabled and post-stroke hemiparetic 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. 308-313). [8009265] (IEEE International Conference on Rehabilitation Robotics). IEEE Computer Society. https://doi.org/10.1109/ICORR.2017.8009265