重庆理工大学学报2026，Vol.40Issue(11)：189-196,210,9.DOI:10.3969/j.issn.1674-8425(z).2026.06.022
融合SSA-GA算法优化XGBoost的sEMG手势识别方法
sEMG gesture recognition based on SSA-GA hybrid algorithm-optimized XGBoost
摘要
Abstract
This paper proposes a hybrid sparrow search algorithm and genetic algorithm-optimized XGBoost model for gesture recognition to address the low accuracy of surface electromyography(sEMG)gesture recognition.First,sEMG signals are preprocessed using notch filtering and Butterworth filtering.Then,an optimal subset of features is selected by SSA,and the classifier hyperparameters are optimized by GA,enabling rapid localization of high-quality parameter regions followed by fine-tuning.The optimized XGBoost model is subsequently applied for gesture classification.Experimental results show the prediction performance of the SSA-GA-optimized XGBoost model is markedly superior to other methods,achieving an average recognition accuracy of 94.37%,up by 3.2%,1.8%,and 2.1%compared with that of the conventional XGBoost,SSA-XGBoost,and GA-XGBoost methods.The proposed approach possesses enhanced anti-overfitting ability,stronger generalization capability,and improved robustness.关键词
表面肌电信号/SSA/遗传算法/XGBoost分类器/手势识别Key words
surface electromyography/sparrow search algorithm/genetic algorithm/xgboost classifier/gesture recognition分类
信息技术与安全科学引用本文复制引用
吉志琦,韩团军,王伊萌,刘庆江,李凯阳..融合SSA-GA算法优化XGBoost的sEMG手势识别方法[J].重庆理工大学学报,2026,40(11):189-196,210,9.基金项目
国家自然科学基金项目(61972239) （61972239）
陕西省教育厅专项科学研究计划项目(18JK0160,18JK0154) （18JK0160,18JK0154）
陕西理工大学科研基金项目(SLG2118,SLG2120) （SLG2118,SLG2120）
