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。
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.
作者
张亚龙
张世武
孙帅帅
曹雨东
陈怡
金虎
卢昀
Zhang Yalong;Zhang Shiwu;Sun Shuaishuai;Cao Yudong;Chen Yi;Jin Hu;Lu Yun(University of Science and Technology of China,Hefei 230031,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2022年第6期13-21,共9页
Journal of Electronic Measurement and Instrumentation
基金
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。
关键词
表面肌电信号
A型超声信号
双传感融合
疲劳检测
surface EMG signal
A-type ultrasound signal
dual sensor fusion
fatigue detection
