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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Vydané v:Journal of King Saud University. Computer and information sciences Ročník 37; číslo 6; s. 149 - 26
Hlavní autori: Lv, Jian, Huang, Binhao, Qiang, Ligang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cham Springer International Publishing 01.08.2025
Springer Nature B.V
Springer
Predmet:
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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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1319-1578
2213-1248
1319-1578
DOI:10.1007/s44443-025-00164-6