Preliminary Evaluation of a Wearable Sensor System for Heart Rate Assessment in Guide Dog Puppies
Bibliographic record
Abstract
This paper details the development of a novel wireless heart rate sensing system for puppies in training as guide dogs. The system includes a harness with on-board electrocardiography (ECG) front-end circuit, inertial measurement unit and a micro-computer with wireless capability where the major research focus of this paper was on the ergonomic design and evaluation of the system on puppies. The first phase of our evaluation was performed on a Labrador Retriever between 12 to 26 weeks in age as a pilot study. The longitudinal weekly data collected revealed the expected trend of a decreasing average heart rate and increased heart rate variability as the age increased. In the second phase, we improved the system ergonomics for a larger scale deployment in a guide dog school (Guiding Eyes for the Blind (Guiding Eyes)) on seventy 7.5-week-old puppies (heart rate coverage average of 86.7%). The acquired ECG based heart rate data was used to predict the performance of puppies in Guiding Eyes's temperament test. We used the data as an input to a machine learning model which predicted two Behavior Checklist (BCL) scores as determined by expert Guiding Eyes puppy evaluators with an accuracy above 90%.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".