Pawlse: Development of Wearable IoT-Based Pet Vest for Pet Health Monitoring System
Bibliographic record
Abstract
This study presents Pawlse, a wearable IoT-based vest developed over a 10-month period from August 2024 to May 2025, designed to enable real-time, non-invasive monitoring of vital signs in small to medium-sized pets. The system integrates a non-contact infrared (IR) temperature sensor and an optical heart rate sensor, connected to an ESP32 microcontroller for seamless wireless data transmission to a mobile application. Prioritizing pet comfort, data reliability, and user accessibility, Pawlse was evaluated through real-world trials involving veterinarians, pet owners, and animal care professionals. Feedback was collected using Likert-scale surveys to assess usability, satisfaction, and perceived utility, while technical performance was benchmarked against the ISO/IEC 25010 software quality model, covering aspects such as functional suitability, reliability, performance efficiency, usability, and compatibility. The findings affirm the feasibility of Pawlse as a practical and user-friendly solution for continuous pet health monitoring, supporting proactive and preventive veterinary care, and contributing meaningfully to the growing field of animal health technology.
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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 | 0.000 |
| 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) | 0.004 | 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".