HOME dataset

The HOME Benchmark is an evaluation-only benchmark designed to assess the cross-device generalization performance of AI models on consumer-grade single-lead ECG waveforms, including recordings from Apple Watch and QOCA ECG102D devices. This benchmark is released to enable fair and standardized model comparison, while explicitly preventing model training, fine-tuning, domain adaptation, or commercial exploitation. Ground-truth labels are intentionally withheld. Model performance is assessed exclusively through a controlled submission and evaluation process.

The HOME Benchmark dataset MUST NOT be used for:

1. Model training of any kind
2. Fine-tuning or partial fine-tuning
3. Domain adaptation or transfer learning
4. Self-supervised, contrastive, or representation learning
5. Feature extractor pretraining
6. Parameter optimization or calibration

This prohibition applies even if ground-truth labels are not provided. Access to waveform data does not imply permission to use the data for learning representations or updating model parameters. The dataset is evaluation-only.

Model performance is evaluated only through centralized scoring (https://ailab.ndmutsgh.edu.tw/app/home-benchmark). Users must submit prediction files for evaluation; labels are never released. To submit predictions for evaluation, users must apply for an account. Account applications must be sent to: Chin Lin — xup6fup0629@gmail.com

Please include:

1. Full name
2. Institutional affiliation
3. Academic email address
4. Intended evaluation task(s)
5. Username

Users who wish to become familiar with the evaluation workflow may log in using the following test account:

Username: test
Password: test

The test account allows unlimited submissions, but only with the system-provided default example data.
Any modification to the example submission files (including changes to values, UIDs, or formatting) will result in automatic rejection and cannot be uploaded. This test account is provided solely for demonstration and system familiarization purposes and does not perform real evaluation.

Researchers who apply for and receive an approved account may submit predictions for official evaluation.
For each approved account:

1. Each task is limited to a maximum of 10 submissions
2. Submissions beyond this limit will not be evaluated
3. The submission cap is strictly enforced

This submission limit is implemented to prevent adaptive probing, iterative attacks, and potential label leakage, thereby preserving the integrity and fairness of the benchmark.