Robotic assessment of the contribution of motor commands to wrist position sense

Sara Contu, Francesca Marini, Lorenzo Masia

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Assessing joint position sense for rehabilitation after neurological injury provides a prognostic factor in recovery and long-term functional outcomes. A common method for testing joint position sense involves the active replication of a joint configuration presented via a passive movement. However, recent evidence showed how this sense is mediated by the centrally generated signals of motor command, such that movements produced volitionally may be coded differently from passive movements and accuracy may be different when matching targets presented actively. To verify this hypothesis we asked ten participants to actively replicate a target wrist angle with the help of a visual feedback in two conditions, which differed in the mode of target presentation: active (aaJPM) or passive (paJPM). The accuracy of target matching, directional bias and variability were analyzed, as well as speed and smoothness of the matching movement and criterion movement in the aaJPM. Overall results indicate higher accuracy and lower variability in the paJPM, while directional bias showed the tendency to overshoot the target regardless of condition. The speed did not differ in the two conditions and movements were smoother in the aaJPM, suggesting a higher confidence by participants in their matching ability. In conclusion, this study suggests that motor commands negatively affect the accuracy of joint position sense when matching involves the integration of visual and proprioceptive information.

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
Pages941-946
Number of pages6
ISBN (Electronic)9781538622964
DOIs
StatePublished - 11 Aug 2017
Externally publishedYes
Event2017 International Conference on Rehabilitation Robotics, ICORR 2017 - London, United Kingdom
Duration: 17 Jul 201720 Jul 2017

Publication series

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

Conference

Conference2017 International Conference on Rehabilitation Robotics, ICORR 2017
Country/TerritoryUnited Kingdom
CityLondon
Period17/07/1720/07/17

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