Ergonomics of Living Environment for the People with Special Needs
Bibliographic record
Abstract
A safe, convenient, sound and healthy living environment is the prerequisite for a good house for the people with special needs. The intention of making a house in such a way that it solves basic problems of fixture and fittings. However the construction phase of a good house is a critical to design inside and outside structures. Often the builders do not know all the factors to be considered that can maintain a safe, hygienic and healthy environment. It is believed that when housing is ergonomically furnished, then a maximum benefit will be achieved. To meet with an individual's specific needs, an analysis of user's requirement is the most important factors to be considered in the design of special houses. Users' data such as anthropometric dimension, users' choices and preferences are also necessary to design a suitable living environment. In this regard, this paper illustrates some ergonomic features to design and develop good houses in terms of how people with restricted mobility and communication can truly be helped residing in their homes and performing their daily living activities. Users' social, medical and engineering needs are highlighted following the process of disability, ageing, or impairments to achieve the maximum level of benefits, and ensuring safe and sound living.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".