Embedded Wearable IoT System for Child Safety Based on Hybrid Deep Learning Classification
Bibliographic record
Abstract
The integration of the Internet of Things (IoT) into everyday life is revolutionizing personal safety and health monitoring.In increasingly busy and distracting urban environment, ensuring child safety exponentially growing to become a serious concern.The objective of this study is to design a wearable IoT system, helping keep track of a child's location and health to support early emergency action.For the child's safety, a simple tracking system app was designed that helps parents set boundaries and receive real time notifications whenever their child leaves the designated area, detected through GPS.To assess health-related risks, the system collects and analyzes seven key physiological and motion signals: acceleration (accel_x, accel_y, accel_z), gyroscopic movement (gyro_x, gyro_y, gyro_z), and heart rate.To improve detection of health anomalies such as minor seizure, a hybrid deep learning framework consisting of convolutional neural networks (CNN) and long short-term memory (LSTM) networks was developed and trained using modified version of the SHAR-100-20 dataset which simulates human activity in individuals with disabilities.A total of 300,000 measurements were sampled from the modified version of the data and divided into 70% for the training and 30% for the testing to train and apply cross validation for evaluation purposes.The proposed system achieved an excellent 99% accuracy in detecting minor seizures.It surpassed other tracking systems by providing better detection rates, greater awareness of what is happening and faster responses.Moreover, the flexible structure is supporting the use in elderly and medical care monitoring, supplying a complete framework for monitoring health and location in an effective way, and offering a comprehensive solution for realtime health and location tracking.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".