MEDVSE - Efficient Deep Learning-based Estimation of the Vital Signs on Smartphones

Official repository of "Efficient Deep Learning-based Estimation of the Vital Signs on Smartphones".

This repository contains the code and the proposed dataset (MTHS)

Checklist

• MTHS dataset
• Finger Videos (New!)
• Code
• Webpage

Code

• Codes are included in the code folder. Please refer to its Readme.md for more detailed information.

Dataset - MTHS:

• This folder contains our dataset
• Each subject has two .npy files: mean RGB signals as signal_x.npy and ground truth labels as label_x.npy, where x is the patient id.
• signal_x.npy contains the mean signals ordered as R, G, and then B sampled at 30Hz.
• label_x.npy contains the ground truth data ordered as HR(bpm) - SpO2(%) Sampled at 1Hz.

New! - Fingertip videos

Due to some requests we now provide the raw fingertip videos.

For downloading videos, please send us an email with your academic email containing your Gmail address.

Donation :)

If you find our dataset useful please consider donation, it would help us a lot.

btc:

• bc1qtgtflqv0laapltmwczfg8ree70mv90fcwvvsd4

eth:

• 0xCa432902f1270AD076814cD77E03Aef2D09dAc19

usdt (trc20):

• TYRPhPT5BZTvn4bcYY4xguL1FSVwchHHrN

License

This project's code is released under the MIT license. Note that the dataset is released under the CC BY-NC-ND license.

Citation

If you use our dataset or find this repository helpful, please consider citing:

@article{samavati2022efficient,
  title={Efficient deep learning-based estimation of the vital signs on smartphones},
  author={Samavati, Taha and Farvardin, Mahdi and Ghaffari, Aboozar},
  journal={arXiv preprint arXiv:2204.08989},
  year={2022}
}

Contact emails

• tahasamavati12@gmail.com
• mahdi.farvardin@gmail.com