TY - GEN
T1 - Learning task-specific models for reach to grasp movements
T2 - 2012 4th IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2012
AU - Liarokapis, Minas V.
AU - Artemiadis, Panagiotis
AU - Katsiaris, Pantelis T.
AU - Kyriakopoulos, Kostas J.
PY - 2012
Y1 - 2012
N2 - A learning scheme based on Random Forests is used to decode the EMG activity of 16 muscles of the human arm-hand system to a continuous representation of kinematics in reach-to-grasp movements in 3D space. Classification methods are used to discriminate between significantly different reach to grasp strategies, formulating a switching mechanism that may trigger the use of position and object-specific decoding models (task-specificity). These task-specific models can achieve better estimation results than the general models for the kinematics of different reach-to-grasp movements. The efficacy of the proposed methodology is assessed through a strict validation procedure, based on everyday life reach-to-grasp scenarios and data not previously seen during training. Finally, for demonstration purposes, the authors teleoperate an arm-hand model in the OpenRave simulation environment using the estimated from the EMG signals human motion.
AB - A learning scheme based on Random Forests is used to decode the EMG activity of 16 muscles of the human arm-hand system to a continuous representation of kinematics in reach-to-grasp movements in 3D space. Classification methods are used to discriminate between significantly different reach to grasp strategies, formulating a switching mechanism that may trigger the use of position and object-specific decoding models (task-specificity). These task-specific models can achieve better estimation results than the general models for the kinematics of different reach-to-grasp movements. The efficacy of the proposed methodology is assessed through a strict validation procedure, based on everyday life reach-to-grasp scenarios and data not previously seen during training. Finally, for demonstration purposes, the authors teleoperate an arm-hand model in the OpenRave simulation environment using the estimated from the EMG signals human motion.
KW - EMG-Based Tele-operation
KW - ElectroMyoGraphy (EMG)
KW - Learning Scheme
KW - Model Switching
KW - Random Forests
KW - Robotic Arm-Hand System
UR - http://www.scopus.com/inward/record.url?scp=84867429656&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84867429656&partnerID=8YFLogxK
U2 - 10.1109/BioRob.2012.6290724
DO - 10.1109/BioRob.2012.6290724
M3 - Conference contribution
AN - SCOPUS:84867429656
SN - 9781457711992
T3 - Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics
SP - 1287
EP - 1292
BT - 2012 4th IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2012
Y2 - 24 June 2012 through 27 June 2012
ER -