To What Extent Can the Use of a Mobility Assistance Dog Reduce Upper Limb Efforts When Manual Wheelchair Users Ascend a Ramp?
Bibliographic record
Abstract
Biomechanical evidence is needed to determine to what extent the use of a mobility assistance dog (AD(Mob)) may minimize mechanical loads and muscular demands at the upper limbs among manual wheelchair users. This study quantified and compared upper limb efforts when propelling up a ramp with and without an AD(Mob) among manual wheelchair users. Ten manual wheelchair users with a spinal cord injury who own an AD(Mob) ascended a ramp with and without their AD(Mob). The movements of the wheelchair and upper limbs were captured and the forces applied at the pushrims were recorded to compute shoulder mechanical loading. Muscular demand of the pectoralis major, anterior deltoid, biceps, and the triceps was normalized against the maximum electromyographic values. The traction provided by the AD(Mob) significantly reduced the total force applied at the pushrim and its tangential component while the mechanical effectiveness remained similar. The traction provided by the AD(Mob) also resulted in a significant reduction in shoulder flexion, internal rotation, and adduction moments. The muscular demands of the anterior deltoid, pectoralis major, biceps, and triceps were significantly reduced by the traction provided by the AD(Mob). The use of AD(Mob) represents a promising mobility assistive technology alternative to minimize upper limb mechanical loads and muscular demands and optimize performance during wheelchair ramp ascent.
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 | 0.000 | 0.002 |
| 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".