R.N. Khushaba, A. Al-Ani, and A. Al-Jumaily (Australia)
Myoelectric control, feature selection, particle swarm.
The myoelectric signals (MES) from human muscles have
been utilized in many applications such as prosthesis
control. The identification of various MES temporal
structures is used to control the movement of prosthetic
devices by utilizing a pattern recognition approach.
Recent advances in this field have shown that there are a
number of factors limiting the clinical availability of such
systems. Several control strategies have been proposed
but deficiencies still exist with most of those strategies
especially with the Dimensionality Reduction (DR) part.
This paper proposes using Particle Swarm Optimization
(PSO) algorithm with the concept of Mutual Information
(MI) to produce a novel hybrid feature selection
algorithm. The new algorithm, called PSOMIFS, is
utilized as a DR tool in myoelectric control problems. The
PSOMIFS will be compared with other techniques to
prove the effectiveness of the proposed method. Accurate
results are acquired using only a small subset of the
original feature set producing a classification accuracy of
99% across a problem of ten classes based on tests done
on six subjects MES datasets.