kegangwangccnu/mmpd_rppg_dataset

Here is Mobile Muti-domain Physiological Dataset collected by Tsinghua University.

MMPD[EMBC 2023]

Abstract

Here is MMPD: Multi-Domain Mobile Video Physiology Dataset collected by Tsinghua University.
The Multi-domain Mobile Video Physiology Dataset (MMPD), comprising 11 hours(1152K frames) of recordings from mobile phones of 33 subjects. The dataset was designed to capture videos with greater representation across skin tone, body motion, and lighting conditions. MMPD is comprehensive with eight descriptive labels and can be used in conjunction with the rPPG-toolbox.

Code is now updated in the rPPG-Toolbox_MMPD file fold, allowing users to choose any combination of multiple labels. More details would be uploaded soon.For those whose have downloaded or prepare to download our dataset: you are recommended to star this repo in case the dataset may be updated.

@misc{tang2023mmpd,
      title={MMPD: Multi-Domain Mobile Video Physiology Dataset}, 
      author={Jiankai Tang and Kequan Chen and Yuntao Wang and Yuanchun Shi and Shwetak Patel and Daniel McDuff and Xin Liu},
      year={2023},
      eprint={2302.03840},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Samples

	LED-low	LED-high	Incandescent	Nature
Skin Tone 3 Stationary
Skin Tone 4 Rotation
Skin Tone 5 Talking
Skin Tone 6 Walking

Access and Usage

This dataset is built for academic use. Any commercial usage is banned.
To access the dataset, you are supposed to download this letter of commitment.
Send an email to [email protected] and cc [email protected] with the signed or sealed protocol as attachment.
There are two kinds of dataset for convenience: full dataset(370G, 320 x 240 resolution ), mini dataset(48G, 80 x 60 resolution ).
There are two ways for downloads： OneDrive and Baidu Netdisk for researchers of different regions. For those researchers at China, hard disk could also be a solution.

Experiment Procedure[Updated]

Distribution

Distribution	Skin Tone				Gender		Glasses Wearing		Hair Covering		Makeup
	3	4	5	6	Male	Female	True	False	True	False	True	False
Number	16	5	6	6	16	17	10	23	8	23	4	29

The Dataset Structure

MMPD_Dataset
├── subject1
    ├── p1_0.mat        # px_y.mat: x refers to the order of subjects, y refers to the order of the experiments, whcich is corresponding to the experiment procedure.
        ├── video       # Rendered images of the subjects at 320 x 240 resolution     [t, w, h, c]
        ├── GT_ppg      # PPG wavefrom signal                                         [t]
        ├── light       # 'LED-low','LED-high','Incandescent','Nature' 
        ├── motion      # 'Stationary','Rotation','Talking','Walking'
        ├── exercise    # True, False
        ├── skin_color  # 3,4,5,6
        ├── gender      # 'male','female'
        ├── glasser     # True, False
        ├── hair_cover  # True, False
        ├── makeup      # True, False
    ├── ... .mat
    ├── p1_19.mat
├── ...
├── subject33

Reading the data example:

import scipy.io as sio
f = sio.loadmat('p1_0.mat')
print(f.keys())

Results(tested on MMPD)

Simplest scenerio

In the simplest scenerio, we only include the stationary, skin tone type 3, and artificial light conditions as benchmark.

METHODS	MAE	RMSE	MAPE	PEARSON
ICA	8.75	12.35	12.26	0.21
POS	7.69	11.95	11.45	0.19
CHROME	8.81	13.18	12.95	-0.03
GREEN	10.57	15.03	14.59	0.23
LGI	7.46	11.92	10.12	0.12
PBV	8.15	11.52	11.04	0.35
TS-CAN(trained on PURE)	1.78	3.57	2.47	0.93
TS-CAN(trained on UBFC)	1.46	3.13	2.04	0.94

Unsupervised Signal Processing Methods

We evaluated six traditional unsupervised methods in our dataset. In the skin tone comparison, we excluded the exercise, natural light, and walking conditions to eliminate any confounding factors and concentrate on the task at hand. Similarly, the motion comparison experiments excluded the exercise and natural light conditions, while the light comparison experiments excluded the exercise and walking conditions. This approach enabled us to exclude cofouding factors and better understand the unique challenges posed by each task.

Supervised Deep Learning Methods

In this paper, we investigated how state-of-the-art supervised neural network performs on MMPD and studied the influence of skin tone, motion, and light. We used the same exclusion criteria as the evaluation on unsupervised methods.

Citation

Title: MMPD: Multi-Domain Mobile Video Physiology Dataset
Jiankai Tang, Kequan Chen, Yuntao Wang, Yuanchun Shi, Shwetak Patel, Daniel McDuff, Xin Liu