Comparison of EMG signal classification algorithms for the control of an upper limb prosthesis prototype
Authors:
- 11 November 2020
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-4
Abstract
In this paper, Machine Learning algorithms (Decision Trees and Support Vector Machines) are proposed and compared to select a classification system for EMG signals to improve the performance of pattern recognition for the control of a prosthesis prototype.The training, validation and signal classification were made using the Classification Learner application of the MatLab software, using a database captured with the commercial myoelectric armband MYO which contain the information of eight different hand movements.The results show that Support Vector Machines algorithms have a better performance than the decision trees, reaching the 99.8% of accuracy with linear and quadratic kernel and the 99.9% using a cubic kernel.Keywords
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