Resilient EMG Classification to Enable Reliable Upper-Limb Movement Intent Detection.

Abstract

Reliable control of assistive devices using surface electromyography (sEMG) remains an unsolved task due to the signal's stochastic behavior that prevents robust pattern recognition for real-time control. Non-representative samples lead to inherent class overlaps that generate classification ripples for which the most common alternatives rely on post-processing and sample discard methods that insert additional delays and often do not offer substantial improvements. In this paper, a resilient classification pipeline based on Extreme Learning Machines (ELM) was used to classify 17 different upper-limb movements through sEMG signals from a total of 99 trials derived from three different databases. The method was compared to a baseline ELM and a sample discarding (DISC) method and proved to generate more stable and consistent classifications. The average accuracy boost of ≈ 10% in all databases lead to average weighted accuracy rates higher as 53,4% for amputees and 89,0% for non-amputee volunteers. The results match or outperform related works even without sample discards.