AUTHOR=Chen Xue , Yuan Zhongxue , Gao Xianzhi , Zhang Yanxin , Liu Chenglin , Huo Bo 

TITLE=Assessment of synergy-assisted EMG-driven NMSK model for upper limb muscle activation prediction in cross-country sit-skiing double poling

JOURNAL=Frontiers in Bioengineering and Biotechnology

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2025.1585127

DOI=10.3389/fbioe.2025.1585127

ISSN=2296-4185

ABSTRACT=IntroductionCross-country sit-skiers are often individuals with spinal cord injuries, cerebral palsy, or lower limb disabilities, relying heavily on upper limb strength to generate propulsion during skiing. However, frequent shoulder joint movements significantly increase the incidence of shoulder joint disorders. Therefore, quantifying muscle forces during movement is crucial for understanding upper limb force generation patterns. Currently, electromyography (EMG)-driven neuromusculoskeletal (NMSK) models are the predominant method for calculating muscle forces and joint moments. However, this approach heavily depends on the quality and quantity of EMG data. Surface electrodes are typically used to collect activity data from superficial muscles, but during dynamic movements, factors such as skin stretching, sweating, or friction may cause electrode detachment or poor contact, leading to EMG signal acquisition failures or data loss. In this study, we propose a synergy-assisted EMG-driven NMSK model to predict the activation patterns of missing muscles for cross-country sit-skiing double poling.MethodsThis method is based on individualized EMG-driven NMSK models constructed for each participant, incorporating data from 10 muscles. By utilizing the activation data of 9 known muscles, the model predicts the activation of one missing muscle through synergy analysis. For synergy method selection, we systematically compared four approaches: Non-negative Matrix Factorization (NMF), Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Factor Analysis (FA).ResultsThe results demonstrated NMF’s superior performance at 5 synergies, accurately predicting any missing muscle activation among 10 muscles (r = 0.79 ± 0.25 vs. 0.14 ± 0.60-0.45 ± 0.63 for alternatives, p < 0.05), with lower errors (RMSE: 0.21 ± 0.11, p < 0.05 vs. ICA/FA, p < 0.1 vs. PCA; MAE: 0.17 ± 0.09, all p < 0.05).ConclusionThis finding validates the effectiveness of the proposed method in predicting upper limb muscle activation during coupled shoulder and elbow joint movements.