Continuous prediction of knee joint angle in lower limbs based on sEMG: a method combining an improved ZOA optimizer and attention-enhanced GRU

Exoskeleton robots have been increasingly applied in mountaineering, rescue, and military scenarios to alleviate physical burden and enhance mobility. This study proposes a novel approach for continuous knee joint angle prediction based on surface electromyography (sEMG), integrating an Improved Zeb...

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Published in:	Journal of King Saud University. Computer and information sciences Vol. 37; no. 6; pp. 149 - 26
Main Authors:	Lv, Jian, Huang, Binhao, Qiang, Ligang
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 01.08.2025 Springer Nature B.V Springer
Subjects:	Accuracy Attention Collaboration Computer Imaging Computer Science Database Management Datasets Efficiency Electromyography Energy consumption Enhanced GRU Exoskeleton robot Exoskeletons Feature extraction Feature selection Genetic algorithms Knee Knee joint angle prediction Machine Learning Mountaineering Neural networks Optimization Optimization algorithms Original Paper Pattern Recognition and Graphics Pattern recognition systems Real time Robots Root-mean-square errors Signal processing Software Engineering/Programming and Operating Systems Surface Electromyography Swarm intelligence algorithm Systems and Data Security Theory of Computation Vision Surface Electromyography Swarm intelligence algorithm Knee joint angle prediction Enhanced GRU Exoskeleton robot
ISSN:	1319-1578, 2213-1248, 1319-1578
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Abstract	Exoskeleton robots have been increasingly applied in mountaineering, rescue, and military scenarios to alleviate physical burden and enhance mobility. This study proposes a novel approach for continuous knee joint angle prediction based on surface electromyography (sEMG), integrating an Improved Zebra Optimization Algorithm (IZOA) with an attention-enhanced Gated Recurrent Unit (GRU) network. The IZOA leverages Tent and Logistic chaotic mappings for improved population diversity and convergence, along with a memory-based strategy to enhance global search capabilities. Experimental evaluations across three motion tasks—level walking, stair ascent, and stair descent—demonstrated that the proposed method achieved a minimum root mean square error (RMSE) of 1.31°, with over 50% reduction in feature dimensionality, significantly outperforming Genetic Algorithm (GA), Zebra Optimization Algorithm (ZOA), Liver Cancer Algorithm (LCA), and Pied Kingfisher Optimizer (PKO). In addition, normalization based on maximal voluntary contraction (MVC) improved model robustness across subjects. The attention-based GRU further enhanced dynamic feature extraction, leading to an average RMSE reduction of 27.2% compared to baseline GRU and Long Short-Term Memory (LSTM) models. These results confirm the effectiveness of the proposed method in achieving accurate, stable, and continuous sEMG-driven knee joint angle prediction, offering strong potential for intelligent control in wearable exoskeleton systems.
AbstractList	Exoskeleton robots have been increasingly applied in mountaineering, rescue, and military scenarios to alleviate physical burden and enhance mobility. This study proposes a novel approach for continuous knee joint angle prediction based on surface electromyography (sEMG), integrating an Improved Zebra Optimization Algorithm (IZOA) with an attention-enhanced Gated Recurrent Unit (GRU) network. The IZOA leverages Tent and Logistic chaotic mappings for improved population diversity and convergence, along with a memory-based strategy to enhance global search capabilities. Experimental evaluations across three motion tasks—level walking, stair ascent, and stair descent—demonstrated that the proposed method achieved a minimum root mean square error (RMSE) of 1.31°, with over 50% reduction in feature dimensionality, significantly outperforming Genetic Algorithm (GA), Zebra Optimization Algorithm (ZOA), Liver Cancer Algorithm (LCA), and Pied Kingfisher Optimizer (PKO). In addition, normalization based on maximal voluntary contraction (MVC) improved model robustness across subjects. The attention-based GRU further enhanced dynamic feature extraction, leading to an average RMSE reduction of 27.2% compared to baseline GRU and Long Short-Term Memory (LSTM) models. These results confirm the effectiveness of the proposed method in achieving accurate, stable, and continuous sEMG-driven knee joint angle prediction, offering strong potential for intelligent control in wearable exoskeleton systems. Abstract Exoskeleton robots have been increasingly applied in mountaineering, rescue, and military scenarios to alleviate physical burden and enhance mobility. This study proposes a novel approach for continuous knee joint angle prediction based on surface electromyography (sEMG), integrating an Improved Zebra Optimization Algorithm (IZOA) with an attention-enhanced Gated Recurrent Unit (GRU) network. The IZOA leverages Tent and Logistic chaotic mappings for improved population diversity and convergence, along with a memory-based strategy to enhance global search capabilities. Experimental evaluations across three motion tasks—level walking, stair ascent, and stair descent—demonstrated that the proposed method achieved a minimum root mean square error (RMSE) of 1.31°, with over 50% reduction in feature dimensionality, significantly outperforming Genetic Algorithm (GA), Zebra Optimization Algorithm (ZOA), Liver Cancer Algorithm (LCA), and Pied Kingfisher Optimizer (PKO). In addition, normalization based on maximal voluntary contraction (MVC) improved model robustness across subjects. The attention-based GRU further enhanced dynamic feature extraction, leading to an average RMSE reduction of 27.2% compared to baseline GRU and Long Short-Term Memory (LSTM) models. These results confirm the effectiveness of the proposed method in achieving accurate, stable, and continuous sEMG-driven knee joint angle prediction, offering strong potential for intelligent control in wearable exoskeleton systems.
ArticleNumber	149
Author	Qiang, Ligang Lv, Jian Huang, Binhao
Author_xml	– sequence: 1 givenname: Jian surname: Lv fullname: Lv, Jian organization: Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University – sequence: 2 givenname: Binhao surname: Huang fullname: Huang, Binhao email: gs.bhhuang23@gzu.edu.cn organization: Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University – sequence: 3 givenname: Ligang surname: Qiang fullname: Qiang, Ligang organization: Guizhou Aerospace Control Technology Co, Ltd
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Snippet	Exoskeleton robots have been increasingly applied in mountaineering, rescue, and military scenarios to alleviate physical burden and enhance mobility. This... Abstract Exoskeleton robots have been increasingly applied in mountaineering, rescue, and military scenarios to alleviate physical burden and enhance mobility....
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SubjectTerms	Accuracy Attention Collaboration Computer Imaging Computer Science Database Management Datasets Efficiency Electromyography Energy consumption Enhanced GRU Exoskeleton robot Exoskeletons Feature extraction Feature selection Genetic algorithms Knee Knee joint angle prediction Machine Learning Mountaineering Neural networks Optimization Optimization algorithms Original Paper Pattern Recognition and Graphics Pattern recognition systems Real time Robots Root-mean-square errors Signal processing Software Engineering/Programming and Operating Systems Surface Electromyography Swarm intelligence algorithm Systems and Data Security Theory of Computation Vision
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Title	Continuous prediction of knee joint angle in lower limbs based on sEMG: a method combining an improved ZOA optimizer and attention-enhanced GRU
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