Surface Electromyography (sEMG) Based Cost-Effective Prosthetic Arm

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2022-01-01
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Surface Electromyography (sEMG) signals are biomedical signals that represent electrical currents generated during muscle activity, and our central nervous system controls these signals. As a result, we can develop a prosthetic arm using EMG technology. Currently, there are many myoelectric prosthetic arms available commercially. However, they are costly for developing countries like Bangladesh, India, and Pakistan. So we developed a cost-effective circuit to extract the electromyography signal from the skin. The signal from this detector circuit is imported in MATLAB using Arduino Uno R3 for signal analysis, such as Fast Fourier Transform (FFT) and Wavelet Transform (1-D), for the classification of features from the signal. We successfully detected two distinct features during the analysis: the Rest position and the Fist position of the hand. So we used the ESP32 microcontroller for the practical implementation of the system as it has better ADC (12-Bit), higher clock speed (80 MHz), better PWM, and lower power consumption. We used a threshold level to distinguish between the Fist and Rest positions. The threshold value was determined using the trial and error method, and this value may vary from person to person. Finally, the ESP32 drives five servo motors fitted inside the InMoov open-source 3D arm after determining the hand position
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TECHNOLOGY::Electrical engineering, electronics and photonics::Electrical engineering
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North South University
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