Simultaneous estimation of human and exoskeleton motion: A simplified protocol

M. T. Álvarez, D. Torricelli*, A. J. Del-Ama, D. Pinto, J. Gonzalez-Vargas, J. C. Moreno, A. Gil-Agudo, Jose L Pons

*Corresponding author for this work

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

6 Scopus citations

Abstract

Adequate benchmarking procedures in the area of wearable robots is gaining importance in order to compare different devices on a quantitative basis, improve them and support the standardization and regulation procedures. Performance assessment usually focuses on the execution of locomotion tasks, and is mostly based on kinematic-related measures. Typical drawbacks of marker-based motion capture systems, gold standard for measure of human limb motion, become challenging when measuring limb kinematics, due to the concomitant presence of the robot. This work answers the question of how to reliably assess the subject's body motion by placing markers over the exoskeleton. Focusing on the ankle joint, the proposed methodology showed that it is possible to reconstruct the trajectory of the subject's joint by placing markers on the exoskeleton, although foot flexibility during walking can impact the reconstruction accuracy. More experiments are needed to confirm this hypothesis, and more subjects and walking conditions are needed to better characterize the errors of the proposed methodology, although our results are promising, indicating small errors.

Original languageEnglish (US)
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
Pages1431-1436
Number of pages6
ISBN (Electronic)9781538622964
DOIs
StatePublished - 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
Country/TerritoryUnited Kingdom
CityLondon
Period7/17/177/20/17

ASJC Scopus subject areas

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

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