Research on the Surface EMG Signal for Human Body Motion Recognizing Based on Arm Wrestling Robot
In this paper, the surface electromyographic (EMG) signals is acquired from the upper limb when the experimenter competes with the arm wrestling robot (AWR) which is integrated with mechanical arm, elbow/wrist force ensors, servo motor, encoder, 3-D MEMS accelerometer, and USB camera. The arm wrestling robot (AWR) is intended to play arm wrestling game with human on a table with pegs for entertainment and human upper limbs muscle modeling. As the EMG signal is a measurement of the anatomical and physiological characteristic of the given muscle, the macroscopical movement patterns of the human body can be classified and recognized. By using the method of wavelet packet transformation (WPT), the high-frequency noises can be eliminated effectively and the characteristics of EMG signals can be extracted. Auto-regressive (AR) model is adopted to effectively simulate the stochastic and non-stationary time sequences using a series of AR coefficients with a typical order. Artificial neural network (ANN) is utilized to distinguish the different force levels and game grades in the scenario of arm-wrestling. To advance the training speed and accurate rate of the motion pattern classification, back-propagation (BP) neural network based on adaptive learning rate algorithm (ALR) is introduced. The advantage of ALR algorithm compared with standard BP algorithm is confirmed by experiments.
AWR EMG signal WPT AR model ANN
Zhen Gao Jianhe Lei Quanjun Song Yong Yu YunJian Ge
State Key laboratory of Robot Sensing System Institute of Intelligent Machines, Chinese Academy of S State Key laboratory of Robot Sensing System Institute of Intelligent Machines, Chinese Academy of S
国际会议
2006 IEEE International Conference on Information Acquisition
山东威海
英文
1269-1273
2006-08-20（万方平台首次上网日期，不代表论文的发表时间）