特征提取
人工智能
模式识别(心理学)
卷积神经网络
计算机科学
接口(物质)
扭矩
脑-机接口
肌电图
特征(语言学)
计算机视觉
手腕
人工神经网络
脑电图
放射科
最大气泡压力法
哲学
物理
精神科
热力学
气泡
医学
并行计算
语言学
心理学
作者
Yang Yu,Chen Chen,Xinjun Sheng,Xiangyang Zhu
标识
DOI:10.1109/smc42975.2020.9283000
摘要
Neural interface using motor units (MUs) decomposed from surface electromyography (sEMG) has provided a novel approach for the intuitive human-robot interaction. However, existing feature extraction methods from decomposed MUs are simplex, ignoring the inherent spatial information and the subtle interactions between different MUs. In this study, we proposed a MU-specific images based scheme for extracting features from decomposed MUs and further estimating wrist torques continuously. Specifically, MU-specific images were reconstructed from decomposed MUs using sEMG and fed into a convolutional neural network for feature extraction and estimating wrist torques. The results demonstrated that the proposed scheme significantly outperformed three conventional regression methods using decomposed spike count features, with R 2 equal to 0.86 ± 0.05 in pronation/supination and 0.90 ± 0.05 in flexion/extension. This study provides a novel scheme for estimation of continuous movement using decomposed MUs and potentially paves the way of neural interface.
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