Power mobility with collision avoidance for older adults: User, caregiver, and prescriber perspectives
Bibliographic record
Abstract
Collision avoidance technology has the capacity to facilitate safer mobility among older power mobility users with physical, sensory, and cognitive impairments, thus enabling independence for more users. Little is known about consumers' perceptions of collision avoidance. This article draws on interviews (29 users, 5 caregivers, and 10 prescribers) to examine views on design and utilization of this technology. Data analysis identified three themes: "useful situations or contexts," "technology design issues and real-life application," and "appropriateness of collision avoidance technology for a variety of users." Findings support ongoing development of collision avoidance for older adult users. The majority of participants supported the technology and felt that it might benefit current users and users with visual impairments, but might be unsuitable for people with significant cognitive impairments. Some participants voiced concerns regarding the risk for injury with power mobility use and some identified situations where collision avoidance might be beneficial (driving backward, avoiding dynamic obstacles, negotiating outdoor barriers, and learning power mobility use). Design issues include the need for context awareness, reliability, and user interface specifications. User desire to maintain driving autonomy supports development of collaboratively controlled systems. This research lays the groundwork for future development by illustrating consumer requirements for this technology.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".