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sEMG pattern recognition based on recurrent neural network
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AI
DOI：10.1016/j.bspc.2021.103048.png)
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Abstract
En 中文
Surface Electromyography (sEMG) signals have a lot of biomedical applications and modern human-machine interactions. sEMG signals received from muscles that require advanced methods for detection, pre-processing, and classification. Current research technologies are focused, principally on deep neural network architectures that collect spatial data from sEMG signals. The main purpose of this paper is, to implement recurrent neural network (RNN) model based on long-term short-term memory (LSTM), Convolution Peephole LSTM and gated recurrent unit (GRU), which used to train sEMG benchmark databases, and find the correlation between the input (sEMG) and outputs (gesture). The following techniques were evaluated by calculating the success of a variety of variables like training time, accuracy loss and hyper-parameters which were applied on six benchmark datasets, in order to demonstrate the validity of these models and their application on human exoskeleton, with prediction accuracy at almost 99.6%.
Keywords:
sEMG
Recurrent neural network
LTSM
Pattern recognition
RNN
Long-short term memory
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