%0 Journal Article 
%A Imatz-Ojanguren, Eukene
%A Irigoyen, Eloy
%A Keller, Thierry
%T Reinforcement Learning for Hand Grasp with Surface Multi-field Neuroprostheses
%D 2017
* Springer International Publishing
%X Hand grasp is a complex system that plays an important role
in the activities of daily living. Upper-limb neuroprostheses aim at restor-
ing lost reaching and grasping functions on people su ering 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 re-
inforcement learning (RL) approach for achieving grasp functions with a
surface multi- eld neuroprosthesis for grasping. Grasps from 20 healthy
subjects were recorded to build a reference for the RL system and then
two di erent award strategies were tested on simulations based on neuro-
fuzzy models of hemiplegic patients. These  rst results suggest that RL
might be a possible solution for obtaining grasp function by means of
multi- eld neuroprostheses in the near future.
%@ 2194-5357
%K Neuroprostheses
%K Functional electrical stimulation
%K Grasp
%K Reinforcement learning
%K Modeling and control
doi 10.1007/978-3-319-47364-2_30
%U http://hdl.handle.net/11556/344
%~ GOEDOC, SUB GOETTINGEN