Evaluation of the feasibility of an error-minimized approach to powered wheelchair skills training using shared control
Bibliographic record
Abstract
BACKGROUND: Powered wheelchairs promote participation for people with mobility limitations. For older adults with cognitive impairment, existing training methods may not address learning needs, leading to difficulty with powered wheelchair skills. Error-minimized training, facilitated by shared control technology, may provide learning opportunities more suited to this population. OBJECTIVE: The objective of this study was to evaluate the feasibility of an error-minimized approach to powered wheelchair skills training using shared control in residential care. Feasibility indicators were hypothesized a priori to be feasible for use in a definitive RCT. METHODS: A 2 × 2 factorial RCT compared an error-minimized powered wheelchair skills training program (Co-pilot) to a control intervention at two doses (6 sessions vs. 12 sessions). Data were collected on the feasibility of study processes (e.g., recruitment), resources (e.g., participant time), management (e.g., technology reliability), and training outcomes (e.g., adverse events, clinical outcomes). RESULTS: Twenty-five older adults with cognitive impairment participated in the study. Technical issues were encountered in 14.5% of training sessions. Participants receiving 6 sessions of training adhered better to the treatment than those receiving 12 sessions. All participants learned the skills required for PWC use with minor errors, regardless of the training method or dose. Co-pilot participants and trainers reported feelings of safety and training benefits with the use of shared control technology. CONCLUSIONS: Individuals with mild to moderate cognitive impairment are able to learn the skills required to drive a powered wheelchair in as few as six training sessions. Further evaluation of the Co-pilot training program is required.IMPLICATIONS FOR REHABILITATIONShared control teleoperation technology may be used to augment learning in older adults with cognitive impairments.Evaluation of the feasibility of use of novel rehabilitation technologies is critical prior to engaging in large-scale clinical research.Individuals with cognitive impairment are able to learn the required skills for operation of a powered wheelchair.
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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 | 0.021 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".