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Miguel Valencia added In_a_similar_study_of__.tex
about 8 years ago
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In a similar study of real-time muscle estimation was conducted by Lozito et al. \cite{Lozito_2015}. In this work, a real-time embedded implementation for a biomechanical model of the leg was created. The neural estimator implemented in the model allowed two configurations of the model: Precision oriented and Performance oriented. According to Hiraiwa et al. \cite{Hiraiwa}, neural networks make EMG pattern recognition much easier and more efficient. Their study was able to successfully accomplish the classification of the 5 finger movements based on the recognition of their patterns.
A notable method in decoding individual flexion and extension movements of each finger has been presented by Tenore et al. \cite{Tenore_2009}. The study was able to decode the finger movements performed by a transradial amputee with greater then 90\% accuracy. This can be achieved by using a neural network classifier trained on a waveform length feature. Further decoding accuracy from a transradial amputee and able-bodied subjects show no statistically significant difference.
On the topic of EMG signal processing for simultaneous myoelectric control, the study by Nielsen et al. \cite{Nielsen_2009} would be noteworthy. Extracted neural-control information from a multi-channel surface electomyographic signal are read as input and then used to train a multilayer perceptron (MLP) neural network.
