TY - GEN
T1 - Reinforcement Learning for Hand Grasp with Surface Multi-field Neuroprostheses
AU - Imatz-Ojanguren, Eukene
AU - Irigoyen, Eloy
AU - Keller, Thierry
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - 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.
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.
KW - Neuroprostheses
KW - Functional electrical stimulation
KW - Grasp
KW - Reinforcement learning
KW - Modeling and control
KW - Neuroprostheses
KW - Functional electrical stimulation
KW - Grasp
KW - Reinforcement learning
KW - Modeling and control
UR - https://www.scopus.com/pages/publications/84992457867
U2 - 10.1007/978-3-319-47364-2_30
DO - 10.1007/978-3-319-47364-2_30
M3 - Conference contribution
SN - 978-3-319-47363-5
SN - 9783319473635
VL - 527
T3 - 2194-5357
SP - 313
EP - 322
BT - unknown
A2 - Lopez-Guede, Jose Manuel
A2 - Herrero, Alvaro
A2 - Quintian, Hector
A2 - Grana, Manuel
A2 - Etxaniz, Oier
A2 - Corchado, Emilio
PB - Springer International Publishing
T2 - International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2016, International Conference on Computational Intelligence in Security for Information Systems, CISIS 2016 and International Conference on European Transnational Education, ICEUTE 2016
Y2 - 19 October 2016 through 21 October 2016
ER -