Fatigue, induced via repetitive upper-limb motor tasks, influences trunk and shoulder kinematics during an upper limb reaching task in a virtual reality environment
Bibliographic record
Abstract
BACKGROUND: Efficient shoulder movement depends on the ability of central nervous system to integrate sensory information and to create an appropriate motor command. Various daily encountered factors can potentially compromise the execution of the command, such as fatigue. This study explored how fatigue influences shoulder movements during upper limb reaching. METHODS: Forty healthy participants were randomly assigned to one of two groups: Control or Fatigue Group. All participants completed an upper limb reaching task at baseline and post-experimental, during which they reached four targets located at 90° of shoulder abduction, 90° external rotation at 90° abduction, 120° scaption, and 120° flexion in a virtual reality environment. Following the baseline phase, the Fatigue Group completed a shoulder fatigue protocol, while Controls took a 10-minute break. Thereafter, the reaching task was repeated. Upper limb kinematic (joint angles and excursions) and spatiotemporal (speed and accuracy) data were collected during the reaching task. Electromyographic activity of the anterior and middle deltoids were also collected to characterize fatigue. Two-way repeated-measures ANOVA were performed to determine the effects of Time, Group and of the interaction between these factors. RESULTS: The Fatigue group showed decreased mean median power frequency and increased electromyographic amplitudes of the anterior deltoid (p < 0.05) following the fatigue protocol. Less glenohumeral elevation, increased trunk flexion and rotation and sternoclavicular elevation were also observed in the Fatigue group (Group x Time interaction, p < 0.05). The Control group improved their movement speed and accuracy in post-experimental phase, while the Fatigue group showed a decrease of movement speed and no accuracy improvement (Group x Time interaction, p < 0.05). CONCLUSION: In a fatigued state, changes in movement strategy were observed during the reaching task, including increased trunk and sternoclavicular movements and less glenohumeral movement. Performance was altered as shown by the lack of accuracy improvement over time and a decrease in movement speed in the Fatigue group.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".