Recognition method of sEMG gesture based on improved deep forest

HUANG Sheng; MAO Jian

doi:10.12299/jsues.22-0173

Volume 37 Issue 2

Jun. 2023

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Article Navigation > Journal of Shanghai University of Engineering Science > 2023 > 37(2): 190-197

HUANG Sheng, MAO Jian. Recognition method of sEMG gesture based on improved deep forest[J]. Journal of Shanghai University of Engineering Science, 2023, 37(2): 190-197. doi: 10.12299/jsues.22-0173

Citation:

HUANG Sheng, MAO Jian. Recognition method of sEMG gesture based on improved deep forest[J]. Journal of Shanghai University of Engineering Science, 2023, 37(2): 190-197. doi: 10.12299/jsues.22-0173

HUANG Sheng, MAO Jian. Recognition method of sEMG gesture based on improved deep forest[J]. Journal of Shanghai University of Engineering Science, 2023, 37(2): 190-197. doi: 10.12299/jsues.22-0173

Citation:

HUANG Sheng, MAO Jian. Recognition method of sEMG gesture based on improved deep forest[J]. Journal of Shanghai University of Engineering Science, 2023, 37(2): 190-197. doi: 10.12299/jsues.22-0173

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Recognition method of sEMG gesture based on improved deep forest

doi: 10.12299/jsues.22-0173

HUANG Sheng,
MAO Jian^,

School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

Received Date: 2022-05-31
Publish Date: 2023-06-20

Abstract

Abstract

In order to improve the accuracy of gesture recognition based on surface electromyography (sEMG), an improved deep forest combined hand motion recognition method was proposed. The extreme gradient boosting (XGBoost) tree was introduced into the deep forest model to form the cascade structure of deep forest together with the random forest and the complete random forest. The deep forest model integrates three different tree-based classifiers at each level, a total of four decision forests including a random forest, an extreme random forest and two extreme gradient boosting trees. The classification performance was improved by using the complementarity between different learning algorithms. In order to evaluate the performance of the model, the sEMG signals of 4 healthy subjects were collected for the verification experiment of hand action recognition, and compared with random forest, support vector machine, one-dimensional and two-dimensional convolutional neural networks algorithms. The result shows that the average recognition accuracy of the method for 16 commonly used hand actions is 94.14%, and the classification accuracy of sEMG signals is high.
- deep forest,
- surface electromyography (sEMG),
- deep learning,
- pattern recognition,
- extreme gradient boosting (XGBoost),
- action classification

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References(26)

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