Task directionality impacts the ability of individuals with chronic hemiparetic stroke to match torques between arms: Preliminary findings

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

4 Scopus citations

Abstract

Post hemiparetic stroke an individual may face difficulty performing bimanual tasks due to an asymmetry in their arms' strengths. Here, we determined whether participants with a strength asymmetry were impaired bi-directionally when matching torques between arms (i.e., paretic arm matches non-paretic arm, non-paretic arm matches paretic arm). Six participants with chronic hemiparetic stroke and four participants without neurological impairments partook in this study. First, we identified the maximum voluntary torque that participants could generate about each elbow joint (τmvt). Then, we determined how accurately and precisely participants could match, bidirectionally, submaximal isometric flexion torques (0.25 · τMVT:Reference) between arms. Results demonstrate that task directionality impacted the ability of our participants with stroke who had a strength asymmetry to match torques between arms; specifically, participants were unimpaired matching to a referenced non-paretic arm yet impaired in the opposite direction. Additionally, results reveal that the degree to which participants overshot the target torque when matching with their non-paretic arm could be predicted based on their strength asymmetry (R2Adjusted = 0.67). We propose that individuals with stroke may avoid torque matching impairments during bimanual tasks by matching their paretic arm to their non-paretic arm.

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
Pages714-719
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
CountryUnited Kingdom
CityLondon
Period7/17/177/20/17

ASJC Scopus subject areas

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

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