The motor nervous system transmits motion control information through nervous oscillations, which causes the synchronous oscillatory activity of the corresponding muscle to reflect the motion response information and give the cerebral cortex feedback, so that it can sense the state of the limbs. This synchronous oscillatory activity can reflect connectivity information of electroencephalography-electromyography (EEG-EMG) functional coupling. The strength of the coupling is determined by various factors including the strength of muscle contraction, attention, motion intention etc. It is very significant to study motor functional evaluation and control methods to analyze the changes of EEG-EMG synchronous coupling caused by different factors. This article mainly introduces and compares coherence and Granger causality of linear methods, the mutual information and transfer entropy of nonlinear methods in EEG-EMG synchronous coupling, and summarizes the application of each method, so that researchers in related fields can understand the current research progress on analysis methods of EEG-EMG synchronous systematically.
At present, the upper limb function of stroke patients is often assessed clinically using a scale method, but this method has problems such as time-consuming, poor consistency of assessment results, and high participation of rehabilitation physicians. To overcome the shortcomings of the scale method, intelligent upper limb function assessment systems combining sensors and machine learning algorithms have become one of the hot research topics in recent years. Firstly, the commonly used clinical upper limb functional assessment methods are analyzed and summarized. Then the researches on intelligent assessment systems in recent years are reviewed, focusing on the technologies used in the data acquisition and data processing parts of intelligent assessment systems and their advantages and disadvantages. Lastly, the current challenges and future development directions of intelligent assessment systems are discussed. This review is hoped to provide valuable reference information for researchers in related fields.
With the aging of the society, the number of stroke patients has been increasing year by year. Compared with the traditional rehabilitation therapy, the application of upper limb rehabilitation robot has higher efficiency and better rehabilitation effect, and has become an important development direction in the field of rehabilitation. In view of the current development status and the deficiency of upper limb rehabilitation robot system, combined with the development trend of all kinds of products of the upper limb rehabilitation robot, this paper designed a center-driven upper limb rehabilitation training robot for cable transmission which can help the patients complete 6 degrees of freedom (3 are driven, 3 are underactuated) training. Combined the structure of robot with more joints rehabilitation training, the paper choosed a cubic polynomial trajectory planning method in the joint space planning to design two trajectories of eating and lifting arm. According to the trajectory equation, the movement trajectory of each joint of the robot was drawn in MATLAB. It laid a foundation for scientific and effective rehabilitation training. Finally, the experimental prototype is built, and the mechanical structure and design trajectories are verified.