| 张亚龙,张世武,孙帅帅,曹雨东,陈 怡,金 虎,卢 昀.融合肌电信号与 A 型超声的新型肌肉疲劳检测方法[J].电子测量与仪器学报,2022,36(6):13-21 |
| 融合肌电信号与 A 型超声的新型肌肉疲劳检测方法 |
| Muscle fatigue detection method with fusion ofEMG signal and A-type ultrasound |
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| DOI： |
| 中文关键词: 表面肌电信号 A 型超声信号 双传感融合 疲劳检测 |
| 英文关键词:surface EMG signal A-type ultrasound signal dual sensor fusion fatigue detection |
| 基金项目:国家自然科学基金（51828503,52005474）、中国科大 爱博智能联合实验室项目资助 |
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| 中文摘要: |
| 为了提高肌肉的疲劳检测效果,提出了一种双传感融合的方式来弥补单传感模式下信息容易丢失的不足。 该方式将表
面肌电信号的时频域特征与 A 型超声信号的肌肉厚度特征多维度融合,实现了双传感疲劳检测新模式。 采用支持向量机和神
经网络多模型训练,表面肌电信号与 A 型超声双传感融合在 3 种疲劳状态下的检测准确率可以达到 85%以上。 相较于仅仅使
用表面肌电信号的时频域特征(76. 99%)与 A 型超声的肌肉厚度(74. 87%)进行疲劳检测,准确率提升了 8% ~ 13%。 结果表明
对于疲劳检测,表面肌电信号与超声信号双传感融合模式比单传感模式更加准确有效。 |
| 英文摘要: |
| In order to improve the effect of muscle fatigue detection, a dual-sensor fusion method is proposed to make up for the
shortcoming that information is easily lost in single-sensor mode. The method realizes a new dual-sensor fatigue detection mode by
integrating the time-frequency domain features of the surface EMG signal with the muscle thickness feature of the A-type ultrasound signal
in multiple dimensions. Using support vector machine and neural network multi-model training, the detection accuracy of surface EMG
and A-type ultrasonic dual-sensor fusion in three fatigue states can reach 85%. Compared with using only the time-frequency domain
features of surface EMG signals ( 76. 99%) and the muscle thickness of A-mode ultrasound ( 74. 87%) for fatigue detection, the
accuracy is increased by 8% ~ 13%. For fatigue detection, the results show that the dual-sensing fusion mode of surface EMG signal and
ultrasonic signal is more accurate and effective than the single-sensing mode. |
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