| With the advent of aging population society as well as the rapid development of transportation, the number of patients with limb movement disorders sharply rises due to physiological function decline or traffic accident. While the patients with knee joint movement disorders are the most in patients with limb movement disorders, which provides a good opportunity for the knee joint rehabilitation robot technology’s rapid development. Due to the correlation between the human muscles and joints and ligaments, there is a certain degree of relevance between the surface Eectromyography (sEMG) and the corresponding muscle activity and joint activity, and reflects the activity state of the muscles and joints. Therefore, with natural action and good bionic performance, EMG control system combined the biological EMG and the rehabilitation robot does good to improve the patients’initiative of participating in the rehabilitation training, and further enhance the effect of lower limb rehabilitation training.This paper intends to research and develop the knee joint rehabilitation robot control system based on the sEMG signal. Through the acquisition of the real-time sEMG signal from the surface of the human lower limb muscles, the feature information of muscle activity associates with knee joint muscles is extracted. And then the supply pressure of the knee joint rehabilitation robot is controlled to realize the intelligent rehabilitation training on the injured joints. The research works of this thesis are shown as follows:(1) This paper intends to illustrate the physiological basis of EMG recognition of human action, and the generating mechanism, characteristics and feature extraction methods of the sEMG signal, which would provide theoretical base for the follow-up sEMG signal’ acquisition and processing, and the EMG-controlled knee rehabilitation robot research.(2) In view of the problems in weak signals, low SNR, easy to be interfered by the surrounding noise and difficult to acquire the sEMG signal, the sEMG signal conditioner is designed in this paper. This signal conditioner mainly consists of the two-stage amplifying circuit and the filter circuit. The preamplifier and the post variable gain amplifier can magnify the sEMG signal to appropriate multiples. Through designing and reasonably installing the high-pass filter,50Hz notch filter and low-pass filter, the low/high frequency interference mixed in the sEMG signal and the50Hz frequency interference operated in the state grid can be effectively removed. On the basis of these studies, the sEMG signal acquisition and processing system based on LabVIEW is developed. This system performs the functions of sEMG signal acquisition, real-time display, signal capture, signal playback, signal analysis and signal storage and so on.(3) The experiment on the biceps femora’s sEMG signal acquisition in the course of the flexion and extension movement made by the different knee joints is conducted to test and verify effectiveness of the sEMG signal acquisition and processing system. In the experiment, sEMG signal is acquired and the technology of EMG feature extraction is studied under the active and passive rehabilitation training mode of the knee joint rehabilitation robot, which lays the foundation for the following sEMG signal’control on the knee joint rehabilitation robot.(4) The force self-adaptive control strategy of the knee joint rehabilitation robot has been put forward. The hardware experiment platform has be built and the software control system based on the LabVIEW has been developed. Through the analysis of the experimental data acquired from the sEMG-controlled knee rehabilitation robot under different threshold value, the feasibility of the EMG-controlled knee rehabilitation robot has been verified. |
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