The Effect of Fatigue on Wheelchair Users’ Upper Limb Muscle Coordination Patterns in Time-Frequency and Principal Component Analysis
Bibliographic record
Abstract
An assessment of shoulder muscle coordination patterns is important to gain insight into muscle fatigue during wheelchair propulsion. The objective of the present study was to quantify muscle coordination changes over time during fatiguing wheelchair propulsion, as the muscles go through distinct levels of fatigue, a) non-fatigued, b) transiting to fatigue and c) fatigued to exhaustion. We recorded surface electromyography (sEMG) signals of the anterior deltoid (AD), middle deltoid (MD), posterior deltoid (PD), infraspinatus (IS), upper trapezius (UT), sternal head of the pectoralis major (PM), biceps brachii (BB), and triceps brachii (TB) during a wheelchair incremental exercise test. Nine wheelchair users with a diagnosis of spina bifida or T6-T12 spinal cord injury volunteered for the study. Oxygen uptake and SmartWheel kinetic parameters were also recorded during the test. EMG signals were processed by wavelet and principal component analysis (PCA), allowing for an assessment of how wheelchair users modify their muscle coordination patterns over time. Analyses of covariance (ANCOVA) were conducted to identify the main effect of fatigue levels on muscle coordination patterns by controlling for the effect of increased workload as covariate. A significant effect of fatigue levels on the PC1 and PC3 loading scores was found after controlling for the effect of increasing workloads (with both cases). In addition, PC3 reflects the most dominant fatigue effect on muscle coordination patterns which are not affected by increased ergometer workload. PC3 indicates muscle imbalance when muscles are fully fatigued and muscle co-contraction when muscles are beginning to fatigue. We conclude that fatigue-related changes in neuromuscular activity during wheelchair propulsion contribute to muscle imbalance and reflect a strategy of stiffening the shoulder joint.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".