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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".