JACIII Vol.26 No.5 pp. 722-730
doi: 10.20965/jaciii.2022.p0722


Extreme Gradient Boosting for Surface Electromyography Classification on Time-Domain Features

Juan Zhao*1,*2,*3, Jinhua She*4,†, Dianhong Wang*1,*2,*3, and Feng Wang*1,*2,*3

*1School of Automation, China University of Geosciences
No.388 Lumo Road, Hongshan, Wuhan 430074, China

*2Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems
Wuhan 430074, China

*3Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education
Wuhan 430074, China

*4School of Engineering, Tokyo University of Technology
1404-1 Katakura, Hachioji 192-0982, Japan

Corresponding author

January 24, 2022
May 12, 2022
September 20, 2022
surface electromyography (sEMG), extreme gradient boosting (XGBoost), signal classification, machine learning, patient with knee osteoarthritis (KOA)

Surface electromyography (sEMG) signals play an essential role in disease diagnosis and rehabilitation. This study applied a powerful machine learning algorithm called extreme gradient boosting (XGBoost) to classify sEMG signals acquired from muscles around the knee for distinguishing patients with knee osteoarthritis (KOA) from healthy subjects. First, to improve data quality, we preprocessed the data via interpolation and normalization. Next, to ensure the description integrity of model input, we extracted nine time-domain features based on the statistical characteristics of sEMG signals over time. Finally, we classified the samples using XGBoost and cross-validation (CV) and compared the results to those produced by the support vector machine (SVM) and the deep neural network (DNN). Experimental results illustrate that the presented method effectively improves classification performance. Moreover, compared with the SVM and the DNN, XGBoost has higher accuracy and better classification performance, which indicates its advantages in the classification of patients with KOA based on sEMG signals.

Classification flow chart

Classification flow chart

Cite this article as:
J. Zhao, J. She, D. Wang, and F. Wang, “Extreme Gradient Boosting for Surface Electromyography Classification on Time-Domain Features,” J. Adv. Comput. Intell. Intell. Inform., Vol.26 No.5, pp. 722-730, 2022.
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