融合Tsallis熵和波动分散熵的肌肉疲劳表征方法

doi:10.3785/j.issn.1008-973X.2025.12.004

浙江大学学报(工学版)

2025, Vol. 59

Issue (12): 2495-2505 DOI: 10.3785/j.issn.1008-973X.2025.12.004

电子与通信工程

融合Tsallis熵和波动分散熵的肌肉疲劳表征方法

董博(

),吕东澔*(

),奚柯阳,李佳豪

内蒙古科技大学自动化与电气工程学院，内蒙古自治区包头 014010

Muscle fatigue characterization method based on Tsallis entropy and fluctuation-based dispersion entropy

Bo DONG(

),Donghao LV*(

),Keyang XI,Jiahao LI

School of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China

全文: PDF(1548 KB) HTML

摘要：

针对目前表面肌电(sEMG)信号在肌肉疲劳识别中存在特征提取信息丢失、灵敏性不足的问题，进而影响分类精度的问题，提出基于Tsallis熵和波动分散熵(FDE)的平滑增强精细复合多尺度Tsallis波动分散熵(RCMTFDE). 该算法通过引入模糊隶属函数，解决了FDE在离散分类中影响sEMG信号连续性的问题，并结合Tsallis熵提升了FDE对非线性复杂系统的灵敏性. 考虑到单一时间尺度分析难以准确表征信号的问题，提出平滑增强的粗粒化方法，来减少粗粒化过程中信号信息泄露和熵值不稳定性，在多尺度下提取出最佳肌肉疲劳特征. 实验结果表明，RCMTFDE在区分非疲劳和疲劳信号时，展示了显著的熵值差异，且在肌肉疲劳量化曲线中表现出明显的疲劳梯度特征. 相较对比算法，该方法在肌肉疲劳分类中取得最高准确率，达到了96.667%.

关键词： 肌肉疲劳; 表面肌电信号; 波动分散熵; Tsallis熵; 多尺度分析

Abstract:

To address the issues of information loss and insufficient sensitivity in the feature extraction of surface electromyography (sEMG) signals, which resulted in low classification accuracy, a smooth enhanced refined composite multiscale Tsallis fluctuation-based dispersion entropy (RCMTFDE) was proposed based on Tsallis entropy and fluctuation-based dispersion entropy (FDE). The issue of discontinuity in sEMG signals caused by FDE in discrete classification was addressed by introducing a fuzzy membership function. Additionally, Tsallis entropy was combined with FDE to enhance its sensitivity to nonlinear complex systems. Considering that single time-scale analysis could not accurately characterize the signals, a smooth enhanced coarse-graining method was proposed. Signal information leakage and entropy instability during the coarse-graining process were reduced, allowing the extraction of optimal muscle fatigue features across multiple scales. Experimental results showed that RCMTFDE demonstrated significant entropy value differences when distinguishing between non-fatigue and fatigue signals, and exhibited clear fatigue gradient characteristics in the muscle fatigue quantification curve. Compared to the reference algorithms, the proposed method achieved the highest accuracy in muscle fatigue classification, reaching 96.667%.

Key words: muscle fatigue surface electromyography signal fluctuation-based dispersion entropy Tsallis entropy multiscale analysis

收稿日期: 2024-12-08 出版日期: 2025-11-25

CLC:

TN 911.7

基金资助: 内蒙古自治区自然科学基金资助项目(2024MS06024)；内蒙古自治区一流学科科研专项项目(YLXKZX-NKD-020)；内蒙古自治区直属高校基本科研业务费资助项目(2023QNJS194).

通讯作者: 吕东澔 E-mail: 2023023236@stu.imust.edu.cn;wsldh2016957@imust.edu.cn

作者简介: 董博（2000—），男，硕士生，从事生物电信号处理研究. orcid.org/0009-0008-4780-4459. E-mail：2023023236@stu.imust.edu.cn

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引用本文:

董博,吕东澔,奚柯阳,李佳豪. 融合Tsallis熵和波动分散熵的肌肉疲劳表征方法[J]. 浙江大学学报(工学版), 2025, 59(12): 2495-2505.

Bo DONG,Donghao LV,Keyang XI,Jiahao LI. Muscle fatigue characterization method based on Tsallis entropy and fluctuation-based dispersion entropy. Journal of ZheJiang University (Engineering Science), 2025, 59(12): 2495-2505.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2025.12.004 或 https://www.zjujournals.com/eng/CN/Y2025/V59/I12/2495

图 1 粗粒化方法对比

图 2 指标随q变化的均值分布

图 3 不同噪声长度下不同熵算法结果分布情况

图 4 不同噪声下不同熵方法的误差条图

图 5 不同尺度因子和长度下的非疲劳和疲劳信号下的熵值对比

图 6 受试者1分析结果

表 1 各受试者肌肉疲劳量化结果

表 2 Mann-Kendall趋势检验结果

表 3 不同特征肌肉疲劳分类结果

图 7 肌肉疲劳分类实验中各方法的ROC曲线对比结果

表 4 RCMTFDE算法的消融实验结果

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