A Hybrid LoRa and Wi-Fi Mesh System for Multi-Floor Elderly Tracking and Monitoring
Bibliographic record
Abstract
The rapid increase in the elderly population globally and within Singapore has underscored the need for effective solutions that promotes both independence and safety for elderly individuals. Many elderly face risks such as getting lost, experiencing falls, or encountering emergencies without immediate assistance, particularly those with dementia. Current tracking and monitoring technologies often face limitations in scalability, accuracy, and affordability, especially in complex indoor environments. To address these challenges, we developed the Elderly Tracking and Monitoring System (ETMS), a hybrid IoT network integrating LoRa and painlessMesh technologies for reliable indoor and multi-floor tracking. ETMS enables caregivers to receive real-time alerts via geofencing when an elderly individual exits predefined safe zones, with a simple interface that supports efficient monitoring. Extensive testing in a simulated residential setup demonstrated the system’s accurate indoor localization and stable performance across multiple floors, despite minor signal interference. ETMS provides a cost-effective, scalable solution, promoting independent living while alleviating the monitoring burden on caregivers.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".