Surface electromyography (sEMG) is an electrophysiological reflection of skeletal muscle contractile activity that can directly reflect neuromuscular activity. It has been a matter of research to investigate feature extraction methods of sEMG signals. In this letter, we propose a feature extraction method of sEMG signals based on the improved small-world leaky echo state network (ISWLESN). The reservoir of leaky echo state network (LESN) is connected by a random network. First, we improved the reservoir of the echo state network (ESN) by these networks and used edge-added probability to improve these networks. That idea enhances the adaptability of the reservoir, the generalization ability, and the stability of ESN. Then we obtained the output weight of the network through training and used it as features. We recorded the sEMG signals during different activities: falling, walking, sitting, squatting, going upstairs, and going downstairs. Afterward, we extracted corresponding features by ISWLESN and used principal component analysis for dimension reduction. At the end, scatter plot, the class separability index, and the Davies-Bouldin index were used to assess the performance of features. The results showed that the ISWLESN clustering performance was better than those of LESN and ESN. By support vector machine, it was also revealed that the performance of ISWLESN for classifying the activities was better than those of ESN and LESN.

中文翻译：

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基于改进小世界泄漏回波状态网络的表面肌电特征提取

表面肌电图（sEMG）是骨骼肌收缩活动的电生理反映，可以直接反映神经肌肉活动。研究 sEMG 信号的特征提取方法一直是一个研究问题。在这封信中，我们提出了一种基于改进的小世界泄漏回波状态网络（ISWLESN）的 sEMG 信号特征提取方法。泄漏回波状态网络 (LESN) 的蓄水池由随机网络连接。首先，我们通过这些网络改进了回声状态网络 (ESN) 的存储库，并使用边缘添加概率来改进这些网络。该思路增强了油藏的适应性、泛化能力和ESN的稳定性。然后我们通过训练得到网络的输出权重，并将其作为特征。我们记录了不同活动中的 sEMG 信号：跌倒、行走、坐下、蹲下、上楼和下楼。之后，我们通过 ISWLESN 提取相应的特征，并使用主成分分析进行降维。最后使用散点图、类可分指数和Davies-Bouldin指数对特征的性能进行评估。结果表明，ISWLESN 的聚类性能优于 LESN 和 ESN。通过支持向量机，还发现ISWLESN在活动分类方面的性能优于ESN和LESN。最后使用散点图、类可分指数和Davies-Bouldin指数对特征的性能进行评估。结果表明，ISWLESN 的聚类性能优于 LESN 和 ESN。通过支持向量机，还发现ISWLESN在活动分类方面的性能优于ESN和LESN。最后使用散点图、类可分指数和Davies-Bouldin指数对特征的性能进行评估。结果表明，ISWLESN 的聚类性能优于 LESN 和 ESN。通过支持向量机，还发现ISWLESN在活动分类方面的性能优于ESN和LESN。