IoT-Enabled Pediatric Pain Care Leveraging Majority Vote-Based Transfer Learning Models
Bibliographic record
Abstract
Internet of Things (IoT) technology has led to several advancements in healthcare such as remote patient monitoring, robotic surgery, and automated fall detection. Pediatric pain care is an important area that encompasses comprehensive practices dedicated to pediatric pain assessment, monitoring, and management. The objective of this paper is to integrate IoT and Artificial Intelligence (AI) to design an automated monitoring and assessment system for pediatric pain care that includes a rapid response to detected pain experiences. To detect pain experiences in infants, we first trained seven Transfer Learning (TL) models (VGG19, DenseNet121, MobileNetv2, InceptionResNetV2, EfficientNetB0, Xception, and ResNet-50) using synthetic images generated with the stable diffusion model. Furthermore, to optimize the model’s performance, we employed a majority vote ensemble method where the final prediction is based on the majority of predictions from these TL models. We analyzed the combined effect of TL models by aggregating predictions from the top three, top five, and all seven TL models through majority voting. Using three TL models, achieved the highest accuracy of ${9 5 . 0 5 \%}$. Additionally, upon detecting signs of pain in infants, our system triggers audio or visual responses and alerts physicians or parents via a mobile app. This integrated approach enhances real-time pain assessment while enabling prompt and personalized interventions in pediatric pain care.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".