ArticleFrontiers in bioengineering and biotechnology2025
Assessment of synergy-assisted EMG-driven NMSK model for upper limb muscle activation prediction in cross-country sit-skiing double poling.
Article in Frontiers in bioengineering and biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Cross-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. Methods: This 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). Results: The results demonstrated NMF's superior performance at 5 synergies, accurately predicting any missing muscle activation among 10 muscles ( Conclusion: This finding validates the effectiveness of the proposed method in predicting upper limb muscle activation during coupled shoulder and elbow joint movements.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.