Perspectives of individuals with incomplete spinal cord injury concerning the usability of lower limb exoskeletons: An exploratory study
Bibliographic record
Abstract
BACKGROUND: Lower limb exoskeletons have been developed to enable individuals with spinal cord injury (SCI) to walk. Currently, they can be used by people with no motor function (SCI ASIA A or B). OBJECTIVES: To present the perspectives of individuals with ASIA C or D incomplete SCI concerning the usability of lower limb exoskeletons to R&D engineers and clinicians working in motor rehabilitation. METHODS: A qualitative exploratory study was conducted using semi-structured individual interviews with three videos showing exoskeletons. RESULTS: The thirteen participants (seven women, mean age [Formula: see text] 50.8 [Formula: see text] 11.7 years) reported both positive and negative perceptions. Three capabilities that can be compensated for and/or improved using an exoskeleton were mentioned, as well as seven life habits. The participants expected lower limb exoskeletons to have 11 technical characteristics. CONCLUSIONS: There is an important gap between the expectations of individuals with incomplete SCI and what can be done with existing lower limb exoskeletons in the community. This indicates that the design of future exoskeletons for individuals with incomplete SCI should allow this population to achieve capabilities and facilitate their life habits, besides the ones they already perform using their current assistive technologies.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".