Abstract:
Recent approaches in multifunction myoelectrically
controlled prosthetic devices revealed that dimensionality
reduction plays a significant role in the overall system
performance. In this paper, a new feature selection method is
developed based on a mixture of particle swarm optimization
(PSO) method and the concept of mutual information (MI).
The new method, termed PSO-MI, is adopted as a
dimensionality reduction tool for myoelectric control. The
PSO-MI employs the MI measure to aid in controlling the
movements of particles in the solution space, thus forming a
kind of a hybrid filter-wrapper method. The new PSO-MI is
able to account for the interaction property between the
features in the selected subset, thus producing high
classification accuracies. A dataset of transient myoelectric
signal (MES) consisting of six classes of hand grasp is
utilized to test the performance of the proposed method. It is
proved that the PSO-MI outperforms other methods adopted
for dimensionality reduction in myoelectric control achieving
95.5% of classification accuracy across six classes problem.
