MTVital - 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 itsReadme.md
for more detailed information.
Dataset - MTHS:
- This folder contains our dataset
- Each subject has two
.npy
files: mean RGB signals assignal_x.npy
and ground truth labels aslabel_x.npy
, wherex
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}
}