TY - GEN
T1 - Learning human reach-to-grasp strategies
T2 - 2012 IEEE International Conference on Robotics and Automation, ICRA 2012
AU - Liarokapis, Minas V.
AU - Artemiadis, Panagiotis K.
AU - Katsiaris, Pantelis T.
AU - Kyriakopoulos, Kostas J.
AU - Manolakos, Elias S.
PY - 2012
Y1 - 2012
N2 - Reaching and grasping of objects in an everyday-life environment seems so simple for humans, though so complicated from an engineering point of view. Humans use a variety of strategies for reaching and grasping anything from the simplest to the most complicated objects, achieving high dexterity and efficiency. This seemingly simple process of reach-to-grasp relies on the complex coordination of the musculoskeletal system of the upper limbs. In this paper, we study the muscular co-activation patterns during a variety of reach-to-grasp motions, and we introduce a learning scheme that can discriminate between different strategies. This scheme can then classify reach-to-grasp strategies based on the muscular co-activations. We consider the arm and hand as a whole system, therefore we use surface ElectroMyoGraphic (sEMG) recordings from muscles of both the upper arm and the forearm. The proposed scheme is tested in extensive paradigms proving its efficiency, while it can be used as a switching mechanism for task-specific motion and force estimation models, improving EMG-based control of robotic arm-hand systems.
AB - Reaching and grasping of objects in an everyday-life environment seems so simple for humans, though so complicated from an engineering point of view. Humans use a variety of strategies for reaching and grasping anything from the simplest to the most complicated objects, achieving high dexterity and efficiency. This seemingly simple process of reach-to-grasp relies on the complex coordination of the musculoskeletal system of the upper limbs. In this paper, we study the muscular co-activation patterns during a variety of reach-to-grasp motions, and we introduce a learning scheme that can discriminate between different strategies. This scheme can then classify reach-to-grasp strategies based on the muscular co-activations. We consider the arm and hand as a whole system, therefore we use surface ElectroMyoGraphic (sEMG) recordings from muscles of both the upper arm and the forearm. The proposed scheme is tested in extensive paradigms proving its efficiency, while it can be used as a switching mechanism for task-specific motion and force estimation models, improving EMG-based control of robotic arm-hand systems.
KW - Boxplot Zones
KW - Classification
KW - ElectroMyoGraphy (EMG)
KW - Learning Scheme
KW - Muscular Co-Activation Patterns
KW - Random Forests
KW - Synergistic Profiles
UR - http://www.scopus.com/inward/record.url?scp=84864495491&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84864495491&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2012.6225047
DO - 10.1109/ICRA.2012.6225047
M3 - Conference contribution
AN - SCOPUS:84864495491
SN - 9781467314039
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 2287
EP - 2292
BT - 2012 IEEE International Conference on Robotics and Automation, ICRA 2012
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 May 2012 through 18 May 2012
ER -