The surface electromyography(sEMG)is one of the basic processing techniques to the gesture recognition because of its inherent advantages of easy collection and non-invasion.However,limited by feature extraction and c...The surface electromyography(sEMG)is one of the basic processing techniques to the gesture recognition because of its inherent advantages of easy collection and non-invasion.However,limited by feature extraction and classifier selection,the adaptability and accuracy of the conventional machine learning still need to promote with the increase of the input dimension and the number of output classifications.Moreover,due to the different characteristics of sEMG data and image data,the conventional convolutional neural network(CNN)have yet to fit sEMG signals.In this paper,a novel hybrid model combining CNN with the graph convolutional network(GCN)was constructed to improve the performance of the gesture recognition.Based on the characteristics of sEMG signal,GCN was introduced into the model through a joint voting network to extract the muscle synergy feature of the sEMG signal.Such strategy optimizes the structure and convolution kernel parameters of the residual network(ResNet)with the classification accuracy on the NinaPro DBl up to 90.07%.The experimental results and comparisons confirm the superiority of the proposed hybrid model for gesture recognition from the sEMG signals.展开更多
应用非线性动力学的方法 ,研究最大和 6 0 % MVC强度肱二头肌静态疲劳负荷及其恢复期表面肌电信号复杂度变化规律 ,探讨肌肉疲劳过程中 s EMG信号变化的可能原因和机制。结论 :s EMG信号 L em pel- Ziv复杂度反映了神经活动策略和神经...应用非线性动力学的方法 ,研究最大和 6 0 % MVC强度肱二头肌静态疲劳负荷及其恢复期表面肌电信号复杂度变化规律 ,探讨肌肉疲劳过程中 s EMG信号变化的可能原因和机制。结论 :s EMG信号 L em pel- Ziv复杂度反映了神经活动策略和神经肌肉功能状态的变化。运动负荷诱发肱二头肌静态疲劳过程中 s EMG信号复杂度随运动负荷时间延长而减小 ,恢复期 s EMG信号复杂度和 MVC均随恢复时间的延长以相似的模式快速恢复 ,提示 ,s展开更多
本研究的目的在于观察动态等速运动诱发肱二头肌疲劳过程中 s EMG信号时频分析指标的动态变化规律 ,确定能够较好地运用于动态肌肉功能评价的 s EMG指标。研究结果表明 ,动态运动过程中时域分析指标 i EMG和 RMS的变化较小且与负荷持续...本研究的目的在于观察动态等速运动诱发肱二头肌疲劳过程中 s EMG信号时频分析指标的动态变化规律 ,确定能够较好地运用于动态肌肉功能评价的 s EMG指标。研究结果表明 ,动态运动过程中时域分析指标 i EMG和 RMS的变化较小且与负荷持续时间无明显线性相关 ;频域分析指标 MPF和 MF变化较大 ,但是只有 MPF的变化与负荷持续时间呈明显线性相关 ;MPF时间序列曲线下降斜率与肌肉的总作功量之间明显相关 。展开更多
基金supported by the Development of Sleep Disordered Breathing Detection and Auxiliary Regulation System Project(No.2019I1009)。
文摘The surface electromyography(sEMG)is one of the basic processing techniques to the gesture recognition because of its inherent advantages of easy collection and non-invasion.However,limited by feature extraction and classifier selection,the adaptability and accuracy of the conventional machine learning still need to promote with the increase of the input dimension and the number of output classifications.Moreover,due to the different characteristics of sEMG data and image data,the conventional convolutional neural network(CNN)have yet to fit sEMG signals.In this paper,a novel hybrid model combining CNN with the graph convolutional network(GCN)was constructed to improve the performance of the gesture recognition.Based on the characteristics of sEMG signal,GCN was introduced into the model through a joint voting network to extract the muscle synergy feature of the sEMG signal.Such strategy optimizes the structure and convolution kernel parameters of the residual network(ResNet)with the classification accuracy on the NinaPro DBl up to 90.07%.The experimental results and comparisons confirm the superiority of the proposed hybrid model for gesture recognition from the sEMG signals.
文摘应用非线性动力学的方法 ,研究最大和 6 0 % MVC强度肱二头肌静态疲劳负荷及其恢复期表面肌电信号复杂度变化规律 ,探讨肌肉疲劳过程中 s EMG信号变化的可能原因和机制。结论 :s EMG信号 L em pel- Ziv复杂度反映了神经活动策略和神经肌肉功能状态的变化。运动负荷诱发肱二头肌静态疲劳过程中 s EMG信号复杂度随运动负荷时间延长而减小 ,恢复期 s EMG信号复杂度和 MVC均随恢复时间的延长以相似的模式快速恢复 ,提示 ,s
