TY - GEN
T1 - Task discrimination from myoelectric activity
T2 - 2013 IEEE 13th International Conference on Rehabilitation Robotics, ICORR 2013
AU - Liarokapis, Minas V.
AU - Artemiadis, Panagiotis
AU - Kyriakopoulos, Kostas J.
PY - 2013
Y1 - 2013
N2 - A learning scheme based on Random Forests is used to discriminate the task to be executed using only myoelectric activity from the upper limb. Three different task features can be discriminated: subspace to move towards, object to be grasped and task to be executed (with the object). The discrimination between the different reach to grasp movements is accomplished with a random forests classifier, which is able to perform efficient features selection, helping us to reduce the number of EMG channels required for task discrimination. The proposed scheme can take advantage of both a classifier and a regressor that cooperate advantageously to split the task space, providing better estimation accuracy with task-specific EMG-based motion decoding models, as reported in [1] and [2]. The whole learning scheme can be used by a series of EMG-based interfaces, that can be found in rehabilitation cases and neural prostheses.
AB - A learning scheme based on Random Forests is used to discriminate the task to be executed using only myoelectric activity from the upper limb. Three different task features can be discriminated: subspace to move towards, object to be grasped and task to be executed (with the object). The discrimination between the different reach to grasp movements is accomplished with a random forests classifier, which is able to perform efficient features selection, helping us to reduce the number of EMG channels required for task discrimination. The proposed scheme can take advantage of both a classifier and a regressor that cooperate advantageously to split the task space, providing better estimation accuracy with task-specific EMG-based motion decoding models, as reported in [1] and [2]. The whole learning scheme can be used by a series of EMG-based interfaces, that can be found in rehabilitation cases and neural prostheses.
KW - ElectroMyoGraphy (EMG)
KW - Learning Scheme
KW - Random Forests
KW - Task Specificity
UR - http://www.scopus.com/inward/record.url?scp=84891112537&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84891112537&partnerID=8YFLogxK
U2 - 10.1109/ICORR.2013.6650366
DO - 10.1109/ICORR.2013.6650366
M3 - Conference contribution
C2 - 24187185
AN - SCOPUS:84891112537
SN - 9781467360241
T3 - IEEE International Conference on Rehabilitation Robotics
BT - 2013 IEEE 13th International Conference on Rehabilitation Robotics, ICORR 2013
Y2 - 24 June 2013 through 26 June 2013
ER -