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Identifying the knee joint angular position under neuromuscular electrical stimulation via long short-term memory neural networks
2020
Research on Biomedical Engineering
Purpose Recurrent neural networks (RNNs) offer a promising opportunity for identifying nonlinear systems. This paper investigates the effectiveness of the long short-term memory (LSTM) RNN architecture in the specific task of identifying the knee joint angular position under neuromuscular electrical stimulation (NMES). The standard RNN model referred to as SimpleRNN and the well-known multilayer perceptron (MLP) are used for comparison purposes. Methods Data from seven healthy and two
doi:10.1007/s42600-020-00089-1
fatcat:d3ja4o555rbp3aiipdyainhgxu