融合肌电和超声射频信号特征的骨骼肌肌力评估研究

Muscle Strength Estimation by Fusion of Surface EMG and Ultrasonic RF Signals

肌力是反映肌肉状态的重要参数,可表征人体的运动功能、肌肉健康状态以及疲劳程度。无创肌力评估技术在体育运动指导、肌肉疾病诊断、康复状态评价等诸多领域具有重要理论意义和广泛应用价值。本研究提出一种基于深度学习算法对表面肌电(sEMG)和超声射频(RF)信号进行特征融合分析的肌力评估方法,利用卷积神经网络(CNN)和池化操作提取信号的有效特征(CNNFeat),并作为支持向量机(SVM)分类器的输入进行处理和分类。该方法利用CNN-SVM网络探究CNNFeat对信号特征的识别能力,并验证sEMG与超声RF信号融合的互补性。实验采集10名健康受试者肱二头肌在不同负荷下的sEMG和超声RF信号。在多用户场景模式下的处理结果表明,CNNFeat与传统肌电信号和超声射频信号的特征相比,能够提高分类性能,具有较强的鲁棒性。肌电信号的准确率为84.23%,超声信号的准确率为89.34%,而融合信号的准确率高达96%,且融合信号的震荡较小,损耗收敛更快。

Muscle strength is an important parameter reflecting the state of muscles,which can characterize human body's motor function,muscle health and fatigue level.Non-invasive muscle strength assessment technology has significance and wide application value in many fields,such as sports guidance,muscle disease diagnosis,and rehabilitation status evaluation.In this paper,we proposed a muscle strength assessment method based on feature fusion analysis of surface electromyography(sEMG)and ultrasound radiofrequency(RF)signals by deep learning algorithms,which utilized convolutional neural network(CNN)and pooling operations to extract the effective features(CNNFeat)of the signals and serve as inputs to a support vector machine(SVM)classifier for processing and classification.The method explored the ability of CNNFeat to recognize signal features using CNN-SVM network to verify the complementary nature of sEMG and ultrasound RF signal fusion.The sEMG and ultrasound RF signals of the biceps brachii muscle of 10 healthy subjects were collected under different loads,and the processing results in the multi-user scenario mode showed that CNNFeat was able to improve the classification performance with strong robustness compared to the features of conventional EMG and ultrasound RF signals.The accuracy was 84.23% for EMG signals and 89.34%for ultrasound signals,while the accuracy of the fused signals was as high as 96%,and the fused signals have less oscillations and faster loss convergence.