Research on the Application of AI Intelligent Machines to Enhance the Quality of Life and Happiness of the Elderly in the Context of Great Health
Bibliographic record
Abstract
With the arrival of the aging society and the continuous improvement of human civilization, people pay more and more attention to the quality of existence, quality of life and happiness index, and the elderly service is becoming a hot issue of social concern.The article proposes a set of intelligent monitoring system for the elderly based on ROS service robot in the context of big health.The system is based on the machine vision following module to design the neural network-based fall detection module and the monitoring module of power consumption abnormality to realize the remote contact method between the elderly and the guardian.The article measures the quality of life and happiness index of 600 elderly people in old age through questionnaires, and systematically understands and comprehensively grasps the influence and effect of the monitoring system proposed in this paper on the quality of life and happiness index of the elderly from seven target levels and several index levels, including the quality of healthy life, economic quality of life, family quality of life, social quality of life, cultural quality of life, personal value realization and sense of identity and belongingness , with more than 97% of the elderly believing that the quality of cultural life has been improved by utilizing this AI intelligent machine.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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".