Usability of a Low-Cost Head Tracking Computer Access Method following Stroke
Bibliographic record
Abstract
Assistive technology devices for computer access can facilitate social reintegration and promote independence for people who have had a stroke. This work describes the exploration of the usefulness and acceptability of a new computer access device called the Nouse™ (Nose-as-mouse). The device uses standard webcam and video recognition algorithms to map the movement of the user's nose to a computer cursor, thereby allowing hands-free computer operation. Ten participants receiving in- or outpatient stroke rehabilitation completed a series of standardized and everyday computer tasks using the Nouse™ and then completed a device usability questionnaire. Task completion rates were high (90%) for computer activities only in the absence of time constraints. Most of the participants were satisfied with ease of use (70%) and liked using the Nouse™ (60%), indicating they could resume most of their usual computer activities apart from word-processing using the device. The findings suggest that hands-free computer access devices like the Nouse™ may be an option for people who experience upper motor impairment caused by stroke and are highly motivated to resume personal computing. More research is necessary to further evaluate the effectiveness of this technology, especially in relation to other computer access assistive technology devices.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".