The Surface EMG Signal Acquisition And Motion Simulation System Design Of The Intelligent Bionic Arm

Nowadays we can take a variety of ways to control the prosthetic. Generally wetake EEG or EMG signals to control the prosthetic, Because of the EEG’sunderdevelopment and difficult to extract. On the social, people mainly use the EMGsignals to control prosthesis. Surface EMG signal is bioelectric signals extracted fromthe skin of the human motion by surface electrodes when the human body in motionor rest. It produced by the control of the brain to the muscles by nerve conduction tothe movement of muscles, and it can represent a different state of motion. We cananalyze extracted to the surface EMG signals to determine the human movement state.And reflect this state by bionic prosthetic truthfully to achieve self-control by bionicprosthesis.Currently mainly used EMG signal extraction systems generally cost moreexpensive. Although the recognition rate of the signal is more accurate, not suitablefor general application in society. So we develop a portable, inexpensive bionic armcontrol system, it is of great significance for the research of intelligent bionic arm.The work in this paper studies is as follows:1. The design of bionic arm control system is constituted by the acquisitionsystem and simulation control system. The simulation system mainly includes twoaspects: the hardware simulation and software simulation. Hardware emulation refersto that the signal collected by the acquisition system for processing by the drivecircuit, and processing results into a bionic arm. Then we can use the signal generatedin the body-movement control the bionic arms in real-time. The software simulationpart is transferring the collected signal to the computer after conversion. Processed bythe computer, and simulate the action on the screen.2. According to the characteristics of the surface EMG such as: low-amplitude,low-frequency and easy to interfere, we should filter the signal to remove noise whenwe amplifying the signal. It can reduce the interference of the noise on the usefulsignal. So, in order not to amplify the noise and the signal, we have adopted theprinciple of multi-level zoom. After the each stage amplification, we should filter thenoise to prevent the interference to the signal caused by the large noise. Filter out thehigh and low frequency noise and50Hz frequency interference, improve theeffectiveness of the signal. 3. In hardware emulation section, we use the hardware process the signal. Weuse the obtained result to control the bionic arm. We found that the signal processingin hardware significantly faster than the software portion. It is possible to improve theoverall control of the real-time transmission, and is conducive to the actualapplication.4. In software simulation part, we convert the analog signals collected by thesystem. It become into a digital signal, so that it can be transferred to the computer forprocessing using Matlab software. Then the software VB calls the treatment results toshow the simulation.5. Finally, we used the software to analyze the circuit and experimental hardwareand software simulation part. The results obtained are able to meet the needs of theoverall system, and prove the effectiveness of the control system.