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A learning scheme for reach to grasp movements: on EMG-based interfaces using task specific motion decoding models.

Abstract：

:A learning scheme based on random forests is used to discriminate between different reach to grasp movements in 3-D space, based on the myoelectric activity of human muscles of the upper-arm and the forearm. Task specificity for motion decoding is introduced in two different levels: Subspace to move toward and object to be grasped. The discrimination between the different reach to grasp strategies is accomplished with machine learning techniques for classification. The classification decision is then used in order to trigger an EMG-based task-specific motion decoding model. Task specific models manage to outperform "general" models providing better estimation accuracy. Thus, the proposed scheme takes advantage of a framework incorporating both a classifier and a regressor that cooperate advantageously in order to split the task space. The proposed learning scheme can be easily used to a series of EMG-based interfaces that must operate in real time, providing data-driven capabilities for multiclass problems, that occur in everyday life complex environments.

journal_name

IEEE J Biomed Health Inform

journal_title

IEEE journal of biomedical and health informatics

authors

Liarokapis MV,Artemiadis PK,Kyriakopoulos KJ,Manolakos ES

doi

10.1109/JBHI.2013.2259594

subject

Has Abstract

pub_date

2013-09-01 00:00:00

pages

915-21

issue

5

eissn

2168-2194

issn

2168-2208

journal_volume

17

pub_type

杂志文章

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