Evaluation of a Contact Sensor Skirt for an Anti-Collision Power Wheelchair for Older Adult Nursing Home Residents With Dementia: Safety and Mobility
Bibliographic record
Abstract
We studied an anti-collision power wheelchair's ability to enable safe, independent mobility in nursing home residents with dementia. The device had a contact sensor skirt that compensated for drivers' absent or delayed responses to obstacles. Safety observations were tracked during device use. In six single-subject studies, distances traveled by residents in manual and anti-collision wheelchairs were compared. Two residents could use the device: One resident's mobility and well-being improved; the other thought it was unhelpful. Another resident with potential for use did not like its usability, speed, and appearance. For two other residents, the device did not compensate for decreased initiation, motor planning, and awareness of obstacles above the sensors. Another resident was withdrawn because of verbal aggression. Interviews and focus groups revealed the device's usefulness. Perceptions of safety were mixed. Further work should improve environmental coverage, sensor skirt reliability, and safety; match technology to the needs of a wider range of residents; and enhance usability, functionality, and acceptance.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".