HFs/Ergonomics of Assistive Technology.
Bibliographic record
Abstract
An assistive device is designed to accommodate the special needs of disability that can help people with physical, mental or cognitive challenges go through their day-to-day activities with less difficulty. An assistive device usually provide alternatives to functional limitations imposed by the client's disorder, and thereby minimising rehabilitation costs. It is therefore important to know about how assistive technology will function in all the possible aspects of such disabilities and impairements. When designing a technical device, particularly in conjunction with the target user group, ergonomic issues are therefore important to find out the extent to which an assistive device is convenient or not, and to check the quality performance of assistive technology. Since the question of the match or mismatch of an assistive device and a disabled person requires much attention, it is therefore suggested that paying attention on how an assistive device be ergonomically designed and developed is important. Ergonomic applications are to be applied for increasing motivation of prospective customers through innovative performance of AT. The authors believe that there are opportunities in ergonomic applications to manufacture an assistive device as unique, cost saving, and allows less exertation and reduces energy consumption when it is used. Hence this paper highlights human factors and/or ergonomics consideration in the process of design and development of assistive devices synchronising with gerontechnological research and development aiming to emphasise user's requirement.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".