A method for extract characteristics of electromyography signal
A technology of myoelectric signal and feature extraction, applied in neural learning methods, biological feature recognition, instruments, etc., can solve the problems of insufficient frequency domain feature extraction and large change of time domain features, so as to reduce input data bandwidth and reduce redundancy. In addition, solve the effect of large changes in time domain characteristics
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[0030] The technical solutions of the present invention will be further elaborated below according to the drawings and in conjunction with the embodiments.
[0031] The myoelectric signal data used in this embodiment is the myoelectric signal data obtained by four people doing ten different gestures. The different gestures of each person represent a category, and each gesture is an 8-dimensional data. The data has 10000 sample points. A feature extraction method for electromyographic signals, constructing a stacked autoencoder network architecture, that is, stacking multiple autoencoders, each autoencoder is a three-layer neural network, setting the parameters of each layer of neural network and using random gradients The descent method (Stochastic gradient descent, SGD) is used to train and update the weights; a restricted Boltzmann machine (RBM) with only the visible layer and the hidden layer is established, and the gradient descent method is used to train and update the we...
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