Toward Limb Position Invariant Myoelectric Pattern Recognition

Abstract—Recent studies on the myoelectric control of powered
prosthetics revealed several factors that affect its clinical
performance. One of the important factors is the variation
in the limb position associated with normal use which can
have a substantial impact on the robustness of Electromyogram
(EMG) pattern recognition. To solve this problem, we propose
in this paper a new feature extraction algorithm based on set of
spectral moments that extracts the relevant information about
the EMG power spectrum in an accurate and efficient manner.
The main goal is to rely on effective knowledge discovery and
pattern recognition methods to discover the neural information
embedded in the EMG signals regardless of the limb position.
Specifically, the proposed features define descriptive qualities for
the general time domain-based characterization of the EMG
spectral amplitude, spectral sparsity, and irregularity factor
by the application of mathematical-statistical methods which
also include frequency consideration. The performance of the
proposed spectral moments is tested on EMG data collected from
eight subjects, while implementing eight classes of movements,
each at five different limb positions

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