Dimensions of the Smart Home Ontology for Elderly People
Bibliographic record
Abstract
According to the official statistics of the country's population, it is predicted that in the next twenty years, more than a quarter of the population will be elderly. In recent years, smart home technology has made significant progress in Iran and its use is expanding. In the coming years, an important and huge part of the smart home market will be aimed at the country's elderly population, and benefiting from the capabilities of this technology without proper design will be far from its ideal state. On the other hand, monitoring the health and care of people who, while lonely, prefer an independent life - or will inevitably be in such a situation - will be one of the main concerns of the country's health system. In smart homes, we are faced with different ontologies. Therefore, studying the dimensions of ontologies and understanding the path of maturity and future trends can be helpful in this field. The present study is a combined study that was carried out in the following three stages, in the first stage, studies designed with the aim of creating an ontology for the smart home to monitor the health of the elderly were extracted and analyzed. In the second stage, the domains and subdomains in the published ontologies were provided to 5 experts, and in the third stage, due to the lack of attention to the mental health dimension and the lack of the spiritual health dimension in the designed ontologies, a review of The studies that have been published in the field of mental health control in the smart home were conducted and also due to the lack of spiritual health dimension, measurable indicators in this dimension of health were extracted by library study. In recent ontologies, the physiological state and behavior of physical activity, nutrition, cognitive and mental state, and social behavior are seen as new dimensions, but without considering spiritual health, other dimensions of human life cannot function properly and as a result, Achieving the highest level of quality of life will not be possible. Based on the results of this article, ontology can be consistent with the definition of health by the World Health Organization, having four dimensions of physical health, mental health, social health, and spiritual health, each of which includes its own subdomains and special variables that are measured through physical tools and Communication in the smart home can be measured, controlled and improved.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".