外骨骼系统中控制信号的分析与处理

【作者】 张旭；

【导师】 韩平畴；

【作者基本信息】 青岛大学 ， 信号与信息处理， 2011， 硕士

【摘要】 本文的主要工作是表面肌电信号采集和处理,及其在外骨骼系统中作为控制信号的应用,主要用于控制手臂和腿部的运动。表面肌电信号是由表面电极检测和记录的由神经肌肉活动产生的生物电信号。表面肌电信号反映肌肉的功能状态。因此,选择表面肌电信号作为控制信号具有直接、自然的特点。实验中的表面肌电信号是由加拿大Thought Technology Ltd.公司推出的MYOTRAC INFINITI Clinical T9850US肌电信号采集设备获得。由于表面肌电信号信息量大,对采集的时间序列信号直接进行分类是不切实际的。因此需要提取信号的特征矢量。实验采集的表面肌电信号主要运用小波变换方法进行处理。然后提取肌电信号的有效特征值,结合BP神经网络进行分类,可以实现腿部不同运动模式的有效识别。此外,我们分析研究了步态周期内表面肌电信号和脚底压力信号的变化规律。基于表面肌电信号的特征矢量,选择支持向量机分类器实现了不同路况的识别。同时,利用ADAMS软件仿真人体步行,获得步态周期内脚底压力信号及对应的左膝的角度信号。然后,利用仿真数据训练自适应神经模糊推理系统(ANFIS)得到了脚底压力信号与人体步行时关节角度间的对应关系。更多还原

【Abstract】 This thesis is concerned with the acquisition and processing of surface electromyogram (sEMG) signals for use in the controls of exoskeleton arm and leg motions. The sEMG signals are recorded by electrodes affixed to the skin surface and they capture the biological signals of the activities of the neuromuscular system. The sEMG signals reflect the muscles functional state. Therefore, using sEMG signals to control the motion of exoskeletons is not only a direct but also, a natural approach. The sEMG signals are obtained using the MYOTRAC INFINTI Clinical T9850US EMG Acquisition Instrument from the Canadian Thought Technology Ltd.Due to the large amount of sEMG signals, it is not practical to feed the time sequence directly into a classifier. Instead, they are mapped into a smaller dimension feature vector. The experimentally acquired sEMG signals are then processed via a wavelet transform method. To extract the effective features from the sEMG signals a pattern recognition method, together wih the BP neural network classifier are applied. In our work, we were able to realize the recognition of different leg movement patterns. Further, we analyzed the change in the regularity of the sEMG signals and plantar pressure signals (PPS) for one gait cycle. Based on the sEMG characteristic vector, we used the support vector machine (SVM) classifier to recognize the road profile. Then, we employed the ADAMS software to perform the dynamics simulation, and obtained the simulated PPS and relative joint angle associated with the human gait. Next, we used the simulation data to train the adaptive neuro-fuzzy inference system (ANFIS), and obtained the relationship between PPS and joint angle for human gait.更多还原