CHILD: AI-Based E-Health Framework for Infant Sleep Disorder Identification in 5G Smart Home
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a new deep learning (DL) model for the detection of infant sleep disorders specific to Confusional Arousals (CA), Leg Restlessness (LR), and Sleep Apnea (SA) in 5G smart homes and e-Healthcare systems integration. The proposed framework, CHILD, consists of four critical layers: specific applications such as Infant Monitoring, Sensor and Data Acquisition, Artificial Intelligence systems and e-Healthcare and Smart Home systems. The smart home part increases the effectiveness of real-time environmental detection, the e-Healthcare system helps to provide convenient communication with doctors. The sleep disorder categorization problem is solved using an enhanced deep learning framework involving LSTM and GRU algorithms; the data are from sensors installed in the smart home., we obtained the 88% accuracy of LSTM model in consideration of the home automation and intelligent e-Healthcare system to enhance infant health and response actions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it