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.

中文翻译：

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弹性EMG分类可实现可靠的上肢运动意图检测

使用表面肌电图（sEMG）可靠地控制辅助设备仍然是一项悬而未决的任务，原因是信号的随机行为阻止了实时控制的可靠模式识别。非代表性样本导致固有的类别重叠，从而产生分类波纹，最常见的替代方法依赖于后处理和样本丢弃方法，这些方法会插入额外的延迟，并且通常无法提供实质性的改进。在本文中，使用了基于极限学习机（ELM）的弹性分类管道，通过来自三个不同数据库的总共99个试验的sEMG信号对17种不同的上肢运动进行分类。将该方法与基线ELM和样品丢弃（DISC）方法进行了比较，并证明可以生成更稳定和一致的分类。在所有数据库中，平均准确率提高≈10％，导致被截肢者的平均加权准确率更高，分别为53.4％和89.0％。即使没有样品丢弃，结果也能匹配或胜过相关工作。