Evaluation of an intelligent wheelchair system for older adults with cognitive impairments
Bibliographic record
Abstract
BACKGROUND: Older adults are the most prevalent wheelchair users in Canada. Yet, cognitive impairments may prevent an older adult from being allowed to use a powered wheelchair due to safety and usability concerns. To address this issue, an add-on Intelligent Wheelchair System (IWS) was developed to help older adults with cognitive impairments drive a powered wheelchair safely and effectively. When attached to a powered wheelchair, the IWS adds a vision-based anti-collision feature that prevents the wheelchair from hitting obstacles and a navigation assistance feature that plays audio prompts to help users manoeuvre around obstacles. METHODS: A two stage evaluation was conducted to test the efficacy of the IWS. Stage One: Environment of Use - the IWS's anti-collision and navigation features were evaluated against objects found in a long-term care facility. Six different collision scenarios (wall, walker, cane, no object, moving and stationary person) and three different navigation scenarios (object on left, object on right, and no object) were performed. Signal detection theory was used to categorize the response of the system in each scenario. Stage Two: User Trials - single-subject research design was used to evaluate the impact of the IWS on older adults with cognitive impairment. Participants were asked to drive a powered wheelchair through a structured obstacle course in two phases: 1) with the IWS and 2) without the IWS. Measurements of safety and usability were taken and compared between the two phases. Visual analysis and phase averages were used to analyze the single-subject data. RESULTS: Stage One: The IWS performed correctly for all environmental anti-collision and navigation scenarios. Stage Two: Two participants completed the trials. The IWS was able to limit the number of collisions that occurred with a powered wheelchair and lower the perceived workload for driving a powered wheelchair. However, the objective performance (time to complete course) of users navigating their environment did not improve with the IWS. CONCLUSIONS: This study shows the efficacy of the IWS in performing with a potential environment of use, and benefiting members of its desired user population to increase safety and lower perceived demands of powered wheelchair driving.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".