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This includes one paper from 2020 that conducted feature extraction using a denoising autoencoder alongside a deep neural network, and a flattened vector and supportvectormachines to evaluate study relevance. This study by Bui et al. This study by Bui et al.
left: neutral pose — do nothing | right: fist — close gripper | Photos from myo-readings-dataset left: extension — move forward | right: flexion — move backward | Photos from myo-readings-dataset This project uses the scikit-learn implementation of a SupportVectorMachine (SVM) trained for gesture recognition. Handel, J. -O.
In their debut paper, they used a support-vectormachine and only messed up 0.8% They didn’t play favorites either — half the training and testing images came from NIST’s original training set and the other half from NIST’s testing set. of the time. Not too shabby, right? But things didn’t stop at MNIST.
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