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 language | English |
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Title of host publication | 2017 International Conference on Rehabilitation Robotics, ICORR 2017 |
Editors | Arash 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 |
Publisher | IEEE Computer Society |
Pages | 308-313 |
Number of pages | 6 |
ISBN (Electronic) | 9781538622964 |
DOIs | |
Publication status | Published - 2017 Aug 11 |
Event | 2017 International Conference on Rehabilitation Robotics, ICORR 2017 - London, United Kingdom Duration: 2017 Jul 17 → 2017 Jul 20 |
Publication series
Name | IEEE International Conference on Rehabilitation Robotics |
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ISSN (Print) | 1945-7898 |
ISSN (Electronic) | 1945-7901 |
Other
Other | 2017 International Conference on Rehabilitation Robotics, ICORR 2017 |
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Country/Territory | United Kingdom |
City | London |
Period | 17/7/17 → 17/7/20 |
Bibliographical note
Publisher Copyright:© 2017 IEEE.
ASJC Scopus subject areas
- Control and Systems Engineering
- Rehabilitation
- Electrical and Electronic Engineering