Usability testing of multimodal feedback interface and simulated collision-avoidance power wheelchair for long-term-care home residents with cognitive impairments
Bibliographic record
Abstract
Many older adults in long-term-care homes have complex physical and cognitive impairments and have difficulty propelling manual wheelchairs. Power wheelchair use is restricted owing to safety concerns. Power wheelchairs with collision-avoidance features are being developed to enable safe and independent mobility; however, a paucity of information exists on interface features to help users navigate away from obstacles. We developed a system combining an interface with auditory, visual, and haptic feedback and a simulated collision-avoidance power wheelchair. This device allowed the investigator to stop movement of the power wheelchair when users approached obstacles and to deliver feedback to help them navigate. Five long-term-care home residents with mild or moderate cognitive impairments evaluated device usability, which included effectiveness, efficiency, and user satisfaction. Each resident used the device for six 1 h sessions. Observations, feedback interviews, and outcome questionnaires were completed during and after the sessions. We found the device effective in enabling residents to achieve basic driving tasks and self-identified indoor mobility goals. Furthermore, residents perceived workload to be low and were satisfied with the device. Residents also felt that the feedback was useful to help them navigate away from obstacles.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".