Official repository of "Efficient Deep Learning-based Estimation of the Vital Signs on Smartphones".
This repository contains the code and the proposed dataset (MTHS)
- MTHS dataset
- Finger Videos (New!)
- Code
- Webpage
- Codes are included in the
code
folder. Please refer to itsReadme.md
for more detailed information.
- 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.
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.
This project's code is released under the MIT license. Note that the dataset is released under the CC BY-NC-ND license.
If you use our dataset or find this repository helpful, please consider citing:
@misc{2204.08989,
Author = {Taha Samavati and Mahdi Farvardin},
Title = {Efficient Deep Learning-based Estimation of the Vital Signs on Smartphones},
Year = {2022},
Eprint = {arXiv:2204.08989},
}