TY  - Conference Object
AU  - Imatz-Ojanguren, Eukene
AU  - Irigoyen, Eloy
AU  - Keller, Thierry
TI  - Reinforcement Learning for Hand Grasp with Surface Multi-field Neuroprostheses
PY  - 2017
PB  - Springer International Publishing
AB  - 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.
UR  - http://hdl.handle.net/11556/344
SN  - 978-3-319-47363-5
ER  -