A learning scheme for reach to grasp movements: On emg-based interfaces using task specific motion decoding models

Minas V. Liarokapis, Panagiotis Artemiadis, Kostas J. Kyriakopoulos, Elias S. Manolakos

Research output: Contribution to journalArticle

27 Scopus citations

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.

Original languageEnglish (US)
Article number6507235
Pages (from-to)915-921
Number of pages7
JournalIEEE Journal of Biomedical and Health Informatics
Volume17
Issue number5
DOIs
StatePublished - 2013

Keywords

  • Electromyography (EMG)
  • learning scheme
  • random forests
  • task specificity

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

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