Abstract
Hand grasp is a complex system that plays an important role in the activities of daily living. Upper-limb neuroprostheses aim at restoring lost reaching and grasping functions on people suffering from neural disorders. However, the dimensionality and complexity of the upper-limb makes the neuroprostheses modeling and control challenging. In this work we present preliminary results for checking the feasibility of using a reinforcement learning (RL) approach for achieving grasp functions with a surface multi-field neuroprosthesis for grasping. Grasps from 20 healthy subjects were recorded to build a reference for the RL system and then two different award strategies were tested on simulations based on neuro-fuzzy models of hemiplegic patients. These first results suggest that RL might be a possible solution for obtaining grasp function by means of multi-field neuroprostheses in the near future.
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Acknowledgements
Authors would like to thank to Intelligent Control Research Group of UPV/EHU for giving the means of carrying out this work and to the Health Division of Tecnalia for its continuous support.
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Imatz-Ojanguren, E., Irigoyen, E., Keller, T. (2017). Reinforcement Learning for Hand Grasp with Surface Multi-field Neuroprostheses. In: Graña, M., López-Guede, J.M., Etxaniz, O., Herrero, Á., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’16-CISIS’16-ICEUTE’16. SOCO CISIS ICEUTE 2016 2016 2016. Advances in Intelligent Systems and Computing, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-319-47364-2_30
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DOI: https://doi.org/10.1007/978-3-319-47364-2_30
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