An integrated care model for service robots in elder care facilities combining sentiment analysis and knowledge-based reasoning: a deep learning framework for long-term care
Bibliographic record
Abstract
The aggravation of population aging makes the demand for elderly care expanding.In this paper, we propose an integrated care model based on deep learning to build an intelligent service robot system for elder care organizations by integrating sentiment analysis and knowledge reasoning techniques.The model is driven by the dynamic needs in long-term care scenarios, and two modules are innovatively designed.In the sentiment analysis module, multimodal sensors (facial expression, audio state, textual content) and graph attention networks are integrated, and global contextual information is modeled on these features to identify long-distance emotional dependencies of the elderly.In the knowledge inference module, graph representation learning is combined with knowledge graph temporal inference to construct an inference model to speculate the care needs of the elderly.The experiment shows that after the system performs long-term service, the depression condition of the elderly is significantly improved, and the nursing care safety risk perception shows a significant difference from that before the system is used (P<0.001).The integrated care model studied in this paper provides a practical technical solution to the problem of aging care resource shortage.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".