Hand Rim Wheelchair Propulsion Training Using Biomechanical Real-Time Visual Feedback Based on Motor Learning Theory Principles
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: As considerable progress has been made in laboratory-based assessment of manual wheelchair propulsion biomechanics, the necessity to translate this knowledge into new clinical tools and treatment programs becomes imperative. The objective of this study was to describe the development of a manual wheelchair propulsion training program aimed to promote the development of an efficient propulsion technique among long-term manual wheelchair users. METHODS: Motor learning theory principles were applied to the design of biomechanical feedback-based learning software, which allows for random discontinuous real-time visual presentation of key spatiotemporal and kinetic parameters. This software was used to train a long-term wheelchair user on a dynamometer during 3 low-intensity wheelchair propulsion training sessions over a 3-week period. Biomechanical measures were recorded with a SmartWheel during over ground propulsion on a 50-m level tile surface at baseline and 3 months after baseline. RESULTS: Training software was refined and administered to a participant who was able to improve his propulsion technique by increasing contact angle while simultaneously reducing stroke cadence, mean resultant force, peak and mean moment out of plane, and peak rate of rise of force applied to the pushrim after training. CONCLUSIONS: The proposed propulsion training protocol may lead to favorable changes in manual wheelchair propulsion technique. These changes could limit or prevent upper limb injuries among manual wheelchair users. In addition, many of the motor learning theory-based techniques examined in this study could be applied to training individuals in various stages of rehabilitation to optimize propulsion early on.
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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) | 0.002 | 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".