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基于表面肌电信号的多任务场景下的步态阶段识别

Gait Phase Recognition in Multi-Task Scenarios Based on sEMG Signals.

作者信息

Shi Xin, Zhang Xiaheng, Qin Pengjie, Huang Liangwen, Zhu Yaqin, Yang Zixiang

机构信息

School of Automation, Chongqing University, Chongqing 400044, China.

Shenzhen Insitute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China.

出版信息

Biosensors (Basel). 2025 May 10;15(5):305. doi: 10.3390/bios15050305.

Abstract

In the human-exoskeleton interaction process, accurately recognizing gait phases is crucial for effectively assessing the assistance provided by the exoskeleton. However, due to the similarity in muscle activation patterns between adjacent gait phases, the recognition accuracy is often low, which can easily lead to confusion in surface electromyography (sEMG) feature extraction. This paper proposes a real-time recognition method based on multi-scale fuzzy approximate root mean entropy (MFAREn) and an Efficient Multi-Scale Attention Convolutional Neural Network (EMACNN), building upon the concept of fuzzy approximate entropy. MFAREn is used to extract the dynamic complexity and energy intensity features of sEMG signals, serving as the input matrix for EMACNN to achieve fast and accurate gait phase recognition. This study collected sEMG signals from 10 subjects performing continuous lower limb gait movements in five common motion scenarios for experimental validation. The results show that the proposed method achieves an average recognition accuracy of 95.72%, outperforming the other comparison methods. The method proposed in this paper is significantly different compared to other methods ( < 0.001). Notably, the recognition accuracy for walking in level walking, stairs ascending, and ramp ascending exceeds 95.5%. This method demonstrates a high recognition accuracy, enabling sEMG-based gait phase recognition and meeting the requirements for effective human-exoskeleton interaction.

摘要

在人机外骨骼交互过程中,准确识别步态阶段对于有效评估外骨骼提供的辅助至关重要。然而,由于相邻步态阶段之间肌肉激活模式的相似性,识别准确率往往较低,这很容易导致表面肌电图(sEMG)特征提取出现混淆。本文基于模糊近似熵的概念,提出了一种基于多尺度模糊近似根均值熵(MFAREn)和高效多尺度注意力卷积神经网络(EMACNN)的实时识别方法。MFAREn用于提取sEMG信号的动态复杂性和能量强度特征,作为EMACNN的输入矩阵,以实现快速准确的步态阶段识别。本研究收集了10名受试者在五种常见运动场景下进行连续下肢步态运动的sEMG信号进行实验验证。结果表明,该方法的平均识别准确率达到95.72%,优于其他比较方法。本文提出的方法与其他方法相比有显著差异(<0.001)。值得注意的是,在平路行走、上楼梯和上坡行走时的识别准确率超过95.5%。该方法具有较高的识别准确率,能够实现基于sEMG的步态阶段识别,满足有效的人机外骨骼交互的要求。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57f7/12109951/d3c7dd12bf2a/biosensors-15-00305-g001.jpg

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