What is it about?
Shoulder movements are not considered for electromyography (EMG) based pattern classification control, due to the difficulty to manufacture three-degrees-of-freedom shoulder prostheses. This work aims at exploring the feasibility of classifying up to nine shoulder movements by processing surface electromyography (sEMG) signals from eight trunk muscles. Experimenting with different pattern recognition methods, two classifiers were developed, considering six different combinations of window sizes and increments, and three feature sets for each channel. Linear Discriminant Analysis (LDA) and Neural Networks (NN) models are reported. Finally reducing the number of acquisition channels was analyzed.
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Why is it important?
It is the first work to analyze EMG signals in a pattern recognition approach. The new data set we built can is available to researchers.
Read the Original
This page is a summary of: Analysis and Comparison of Features and Algorithms to Classify Shoulder Movements From sEMG Signals, IEEE Sensors Journal, May 2018, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/jsen.2018.2813434.
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