Time-dependent spectral features for limb position invariant myoelectric pattern recognition

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. Practical results indicate that training the classifier on the EMG moments collected from multiple positions and testing on completely unseen positions can achieve significant reduction in the classification error rates of upon ≈10% on average across all subjects and limb positions.