Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals
Lin Chen, Jianting Fu, Yuheng Wu, Haochen Li, Bin Zheng
Abstract
By training the deep neural network model, the hidden features in Surface Electromyography(sEMG) signals can be extracted. The motion intention of the human can be predicted by analysis of sEMG. However, the models recently proposed by researchers often have a large number of parameters. Therefore, we designed a compact Convolution Neural Network (CNN) model, which not only improves the classification accuracy but also reduces the number of parameters in the model. Our proposed model was validated on the Ninapro DB5 Dataset and the Myo Dataset. The classification accuracy of gesture recognition achieved good results.
Topics & Concepts
ElectromyographyConvolutional neural networkComputer scienceArtificial intelligenceGesture recognitionPattern recognition (psychology)GestureConvolution (computer science)Hidden Markov modelArtificial neural networkMotion (physics)Speech recognitionSurface (topology)MathematicsPhysical medicine and rehabilitationMedicineGeometryMuscle activation and electromyography studiesHand Gesture Recognition SystemsAdvanced Sensor and Energy Harvesting Materials