Abstract
Recent projects highlight how motor learning and a high level of attention control can potentially improve submaximal force production during recovery after stroke. This study focuses on the assessment of detailed metrics of force production and position control -healthy subjects- and their correlation with submaximal force production control learning during a new task consisting in maintaining the position for early rehabilitation after stroke.
We used a Motorized Ankle Foot Orthosis (MAFO) with zero-torque control together with a visual paradigm interface to exert controlled torque profiles to the ankle of the subject. The subject is asked to follow the trajectories in the visual interface, while the robot disturbs the movement. The aim of the exercise is to improve the motor control by learning how to maintain the position to follow the trajectory, compensating the perturbations, in three possible training paradigms: (1) fixed torque, (2) progressive increase of torque, and (3) modulated torque based on score on the task.
All training paradigms led to an improvement in the score comparing pre and post-training performances, so we concluded that this platform induces learning on healthy subjects.
To sum up, we conclude that this tool is useful to induce learning in healthy subjects, and thus will keep improving the training paradigms, for the translation into a rehabilitative tool.
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Acknowledgements
This research has been funded by the Commission of the European Union under the BioMot project - Smart Wearable Robots with Bioinspired Sensory-Motor Skills (Grant Agreement number IFP7-ICT- 2013-10-611695, and partially supported with grant RYC-2014-16613 by Spanish Ministry of Economy and Competitiveness.
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Asín-Prieto, G., González, J.E., Pons, J.L., Moreno, J.C. (2018). Robotic Platform with Visual Paradigm to Induce Motor Learning in Healthy Subjects. In: Ollero, A., Sanfeliu, A., Montano, L., Lau, N., Cardeira, C. (eds) ROBOT 2017: Third Iberian Robotics Conference. ROBOT 2017. Advances in Intelligent Systems and Computing, vol 694. Springer, Cham. https://doi.org/10.1007/978-3-319-70836-2_47
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