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yang-ai-lab/SensorGen

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SensorGen

![Paper](#) ![Webpage](#) ![GitHub](https://github.com/yang-ai-lab/SensorGen) ![HuggingFace](https://huggingface.co/yang-ai-lab/SensorGen-SiT) ![Python](https://www.python.org/) ![PyTorch](https://pytorch.org/)

This repository hosts the pre-trained checkpoints used in the SensorGen study ("Signal or Noise? Understanding Generative Models for Real-World Sensor Time Series").

Checkpoint	Task	Dataset
`text2ecg.pt`	Text-to-ECG	MIMIC-IV ECG
`bp_translation.pt`	PPG and NIBP to invasive BP	VitalDB

Usage

Pair this checkpoint repository with the GitHub code release at [yang-ai-lab/SensorGen](https://github.com/yang-ai-lab/SensorGen).

Download a single checkpoint

python

from huggingface_hub import hf_hub_download

ckpt_path = hf_hub_download(
    repo_id="yang-ai-lab/SensorGen",
    filename="text2ecg.pt",
)

Download all checkpoints

bash

hf download yang-ai-lab/SensorGen --local-dir ./ckpts

Task specifications

Task	Target `x`	C × T	`c_1` (sparse)	`c_2` (dense)
Text-to-ECG	12-lead ECG, 10 s @ 100 Hz	12 × 1,000	Free-text ECG report (CLIP-encoded)	—
PPG → invasive BP	Arterial blood pressure, 30 s @ 50 Hz	1 × 1,500	6-D non-invasive BP statistics	PPG waveform, 1 × 1,500

Datasets

Neither MIMIC-IV ECG nor VitalDB are redistributed in this repository. Credentialed access is required from the original data providers:

—MIMIC-IV ECG — PhysioNet credentialed access
—VitalDB — vitaldb.net

Preprocessing pipelines that convert the raw releases into the HDF5 layout consumed by these checkpoints are documented in the GitHub README.

Limitations and responsible use

—The generated waveforms reflect statistical patterns in the training corpus and must not be used for clinical diagnosis or as a substitute for real patient recordings.
—These models are released for research use. They are not approved medical devices and have not been evaluated for clinical safety or efficacy.

Citation

If you use any of these checkpoints, please cite the SensorGen paper:

bibtex

@article{shuai2026sensorgen,
  title={Signal or Noise? Understanding Generative Models for Real-World Sensor Time Series},
  author={Shuai, Zitao and Xu, Zongzhe and Wu, Yuntian and Li, Sirui and Li, Tianhong and Yang, Yuzhe},
  journal={arXiv preprint arXiv:2607.04245},
  year={2026}
}

License

This release is distributed under the MIT License.