Correlating Data-Driven Muscle Selection Approaches to Synergies for Gait Prediction

Optimizing sensors for physiological input is critical to enhance performance as well as minimize the cost and complexity of assistive devices (e.g. lower-limb exoskeletons). Electromyography (EMG) data can trace muscle activation for gait kinematics prediction. However, identifying optimal muscle g...

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Published in:IEEE transactions on neural systems and rehabilitation engineering Vol. 33; pp. 945 - 955
Main Authors: Guez, Annika, Sebastian Mancero Castillo, C., Hodossy, Balint, Farina, Dario, Vaidyanathan, Ravi
Format: Journal Article
Language:English
Published: United States IEEE 2025
Subjects:
Accuracy
Adult
Algorithms
Biomechanical Phenomena
channel selection
Electrodes
Electromyography
Electromyography (EMG)
Electromyography - methods
Feature extraction
Female
Gait - physiology
Humans
Knee
Knee Joint - physiology
Legged locomotion
Male
multi-layer perceptron
Muscle, Skeletal - physiology
Muscles
Physiology
Principal Component Analysis
recursive feature elimination
Reproducibility of Results
Sensors
Young Adult
ISSN:1534-4320, 1558-0210, 1558-0210
Online Access:Get full text
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Summary:Optimizing sensors for physiological input is critical to enhance performance as well as minimize the cost and complexity of assistive devices (e.g. lower-limb exoskeletons). Electromyography (EMG) data can trace muscle activation for gait kinematics prediction. However, identifying optimal muscle groups for electrode placement and the potential variance between users has not yet been established. In this study, we use data-driven channel selection techniques on EMG signals to find muscle group combinations that maximize prediction performance. We apply greedy search (Recursive Feature Elimination, RFE) and variance-based (Principal Component Analysis, PCA) methods to select muscle groups during gait, without prior knowledge of musculoskeletal inter-connectivity. The selected muscle subsets are evaluated using the normalized accuracy of a Multi-Layer Perceptron (MLP), mapping muscle activity to knee flexion angle in a one-step-ahead scheme. The RFE selection led to an average predicted knee angle validation accuracy of <inline-formula> <tex-math notation="LaTeX">4.52\pm 1.85 </tex-math></inline-formula> % higher than the PCA approach, suggesting that dynamic search is more appropriate than a variance analysis of the signals. Whilst the RFE-selected muscle groups differed across subjects, the selected muscles were consistently spread out over more than 80% of the extracted synergy groups. This study underlines the value of incorporating synergistic information when developing gait prediction models, and reveals that maximizing the number of synergy groups could constitute the basis of muscle selection frameworks.
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ISSN:1534-4320
1558-0210
1558-0210
DOI:10.1109/TNSRE.2025.3543743