Connecting Smart Homes to Healthcare Services for People with Neurodegenerative disorders.
Bibliographic record
Abstract
The objective of this research is to provide an approach for the design of what we have called ‘connected homes’ with a study case for elderly people with dementia living alone. These homes would be connected to a center of surveillance for direct and automatic view of multiple status of the day such as patient security and general health indicators (body temperature, heart rate, blood pressure etc.), detect the intake of meals, eating motivation, humor detection prevention of falls, Alcohol consumption detection, safe use of medicines and emergency situations and other Human Activity Recognition (HAR). The model may also predict situations by using past data accumulation. The model could even send alerts in case of emergency. This service would mean that there would minimum intervention from caregivers thanks to the Artificial Intelligence. As a case study, we proposed a new approach for the conception of connected homes for people with dementia to a central office for automatic human activity detection and help and support accordingly. Such conception includes home design concepts according to standard recommendations and the implementation of new added assistive technology tools to permit the automatic surveillance without violating the ethic requirements. Two installation models will be proposed to consider the financial situation of the patient: a unit or appliance at the patient’s home or a home that is connected to a central office.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".