Automatic Energy Food Estimation In Elderly People With Neurodegenerative Disorders
Bibliographic record
Abstract
Dementia has been an increasing trend with an increase in the population of elderly people, and WHO estimates that the number of individuals with dementia doubles every 20 years. There is no curative solution for dementia, but non-drug approaches can improve patient quality of life. In nursing homes (NH) almost 86% of patients with advanced dementia have problems eating and they need eating assistance. Patients with cognition impairment sometimes confuse food and they do not know when and how much they should eat and drink, so it leads to dehydration and malnutrition which cause weight loss, infection, decreased quality of life and increased risk of death. So, 24-hours caregivers are needed in this situation, and it is so hard for caregivers. Patients with dementia can live easier in the familiar environment, so ATs (Assistive Technologies) can help patients and caregivers to live in their own homes if possible. One of the approaches for monitoring eating activity of people with dementia is calculating calorie of food, the aim of this research is working on it. There are different approaches for measuring calorie of food but most of them depend on the user or they do not consider human value and ethical considerations in their design. Patients with dementia lose their autonomy, so they need an automatic system for calculating calorie of food. The objective of this research is to provide the state of art of the energy food estimation in elderly people with dementia. This study will be a good start for defining our own approaches in the domain.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".