In the face of the large number of people with motor function disabilities,rehabilitation robots have attracted more and more attention.In order to promote the active participation of the user's motion intention i...In the face of the large number of people with motor function disabilities,rehabilitation robots have attracted more and more attention.In order to promote the active participation of the user's motion intention in the assisted rehabilitation process of the robots,it is crucial to establish the human motion prediction model.In this paper,a hybrid prediction model built on long short-term memory(LSTM)neural network using surface electromyography(sEMG)is applied to predict the elbow motion of the users in advance.This model includes two sub-models:a back-propagation neural network and an LSTM network.The former extracts a preliminary prediction of the elbow motion,and the latter corrects this prediction to increase accuracy.The proposed model takes time series data as input,which includes the sEMG signals measured by electrodes and the continuous angles from inertial measurement units.The offline and online tests were carried out to verify the established hybrid model.Finally,average root mean square errors of 3.52°and 4.18°were reached respectively for offline and online tests,and the correlation coefficients for both were above 0.98.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the National Key Research and Development Program of China(No.2020YFC2007500)the Science and Technology Commission of Shanghai Municipality(No.20DZ2220400)。
文摘In the face of the large number of people with motor function disabilities,rehabilitation robots have attracted more and more attention.In order to promote the active participation of the user's motion intention in the assisted rehabilitation process of the robots,it is crucial to establish the human motion prediction model.In this paper,a hybrid prediction model built on long short-term memory(LSTM)neural network using surface electromyography(sEMG)is applied to predict the elbow motion of the users in advance.This model includes two sub-models:a back-propagation neural network and an LSTM network.The former extracts a preliminary prediction of the elbow motion,and the latter corrects this prediction to increase accuracy.The proposed model takes time series data as input,which includes the sEMG signals measured by electrodes and the continuous angles from inertial measurement units.The offline and online tests were carried out to verify the established hybrid model.Finally,average root mean square errors of 3.52°and 4.18°were reached respectively for offline and online tests,and the correlation coefficients for both were above 0.98.
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
