Electromyography control of robotic systems
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Abstract
This work describes research of myoelectric interfaces and their application for controlling robotic systems. Hand gesture data collection software has been created. The neural network was designed and trained to recognize various gestures. The accuracy was 0.96 for four gestures and 0.925 for seven gestures. The prototype of myoelectric signals controlled robot with two degrees of freedom was created. Wireless direct control via bluetooth was implemented.
This work describes research of myoelectric interfaces and their application for controlling robotic systems. Hand gesture data collection software has been created. The neural network was designed and trained to recognize various gestures. The accuracy was 0.96 for four gestures and 0.925 for seven gestures. The prototype of myoelectric signals controlled robot with two degrees of freedom was created. Wireless direct control via bluetooth was implemented.
This work describes research of myoelectric interfaces and their application for controlling robotic systems. Hand gesture data collection software has been created. The neural network was designed and trained to recognize various gestures. The accuracy was 0.96 for four gestures and 0.925 for seven gestures. The prototype of myoelectric signals controlled robot with two degrees of freedom was created. Wireless direct control via bluetooth was implemented.
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electromyography, Myo Armband, robotic control, neural networks, electromyography, Myo Armband, robotic control, neural networks