Remote Biosensing

---

Feel free to contact us with any questions and suggestions. We welcome your contributions and cooperation.

Remote Biosensing (rPPG) is an open-source framework for remote photoplethysmography (rPPG) and non-invasive blood pressure measurement (CNIBP) technology. We aim to implement, evaluate, and benchmark DNN models for remote photoplethysmography (rPPG) and continuous non-invasive blood pressure (CNIBP). Our code is based on PyTorch.

Reference

Remote Bio-Sensing: Open Source Benchmark Framework for Fair Evaluation of rPPG, https://arxiv.org/abs/2307.12644

@misc{kim2023remote,
      title={Remote Bio-Sensing: Open Source Benchmark Framework for Fair Evaluation of rPPG}, 
      author={Dae Yeol Kim and Eunsu Goh and KwangKee Lee and JongEui Chae and JongHyeon Mun and Junyeong Na and Chae-bong Sohn and Do-Yup Kim},
      year={2023},
      eprint={2307.12644},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}

Quick Environment Setting with ANACONDA

conda env create -f rppg.yaml

conda activate rppg

Quick Environment Setting with Docker

docker build -t rppg_docker_test .

docker run rppg_docker_test

docker exec -it {container_name} /bin/bash

conda activate rppg

Build the rPPG !!

Quick Start with our examples

• rPPG( remote PPG) models

year	type	model	implement	paper
2018	DL	DeepPhys	O	paper
2020	DL	MTTS	O	paper
2020	DL	MetaPhys	O	paper
2021	DL	EfficentPhys	O	paper
2023	DL	BIGSMALL	O	paper
2019	DL	STVEN_rPPGNET		paper
2019	DL	PhysNet	O	paper
2019	DL	2D PhysNet + LSTM		paper
2020	DL	Siamese-rPPG		paper
2022	DL	PhysFormer	O	paper
2023	DL	PhysFormer++		paper
2022	DL	APNET	O	paper
TBD	DL	APNETv2		paper
2019	DL	RhythmNet		paper
2020	DL	HeartTrack		paper
2021	DL	TransrPPG		paper
2022	DL	And-rPPG		paper
2022	DL	JAMSNet	O	paper
2023	DL	CRGB rPPG		paper
2023	DL	Skin + Deep Phys		paper
2023	DL + TR	rPPG-MAE		paper
2023	DL	LSTC-rPPG	need to verify	paper
2008	TR	GREEN	O	paper
2010	TR	ICA		paper
2011	TR	PCA	O #Need to change to cuda	paper
2013	TR	CHROM	O	paper
2014	TR	PBV	O	paper
2016	TR	POS	O	paper
2015	TR	SSR	O	paper
2018	TR	LGI	O	paper
2021	TR	EEMD-MCCA		paper
2023	TR	EEMD + FastICA		paper

• rPPG

2023/CVPRW/Real-Time Estimation of Heart Rate in Situations Characterized by Dynamic Illumination using Remote Photoplethysmography/paper

2023/IEEE Access/Heart Rate Estimation From Remote Photoplethysmography Based on Light-Weight U-Net and Attention Modules/paper 2023/IEEE Transation/SSL/Facial Video-based Remote Physiological Measurement via Self-supervised Learning/paper

• CNIBP (Continuous non-invasive blood pressure)

• PP-Net example paper

DATASET INFO

• rPPG datasets

#	Must Need	year	subject	video	label	Dataset	example	config	paper	download or apply
						ALL	example	config
1	△	2011	27	RGB	ECG	MAHNOB_HCI	example	config	link	link
2		2014	25	RGB	PPG	AFRL	example	config	link	link
3	O	2014	10	RGB	PPG/SPo2	PURE	example	config	link	link
4		2016	140	RGB/NIR	PPG/HR/BP	BP4D+	example	config	link	link
5	O	2016	40	RGB	HR/BP	MMSE-HR	example	config	link	link
6	O	2017	40	RGB	PPG/HR/RR	COHFACE	example	config	link	link
7		2017	-	-	PPG/BP	BIDMC	example	config	link	link
8	△	2018	25	RGB	-	LGGI	example	config	link	link
9	O	2018	107	-	PPG/HR	VIPL-HR	example	config	link	link
10		2018	100	RGB/NIR	PPG/HR/HRV/ECG	OBF	example	config	link	link
11		2018	8	RGB/NIR	PPG/HR	MR-NIRP(ind)	example	config	link	link
12	O	2019	42	RGB	PPG/HR	UBFC-rppg	example	config	link	link
13		2020	10	RGB	PPG/HR/ECG	VicarPPG	example	config	link	link
14		2020	18	RGB/NIR	PPG/HR	MR-NIRP(DRV)	example	config	link	link
15	△	2021	56	RGB	PPG/HR/EDA	UBFC-phys	example	config	link	link
16		2021	9	RGB	PPG/HR/HRV	MPRSC-rPPG	example	config		link
17	△	2021	140	RGB/NIR	HR/RR/BP	V4V	example	config	link	link
18		2022	62	RGB	PPG/RR	MTHS	example	config	link	link
19	△	2023	33	RGB	PPG	MMPD	example	config	link	link
20			20	RGB	PPG/HR	EatingSet	example	config		link
21			24	RGB	HR/HRV/ECG	StableSet	example	config		link
22			37	RGB	PPG	BSIPL-RPPG	example	config		link
23			14	-	PPG/HR	BAMI-rPPG	example	config		link
24		2023	890	RGB	PPG/HR/SpO2/BP	Vital Videos	example	config	link	link
25		2011	874	RGB	ECG/Emotion	DEAP	example	config	link

Documentation(TBD)

Performance Comparison

- rPPG

• All evaluations are based on the model with the lowest loss value during validation.

• ! Notice: BigSmall Model was not implemented as Multi-Task learning

  • Test Results - Dataset

  • Test Results - Eval Time Length

• Test Results

MODEL	TRAIN	TEST	IMG_SIZE	EVAL_TIME_LENGTH	MAE	RMSE	MAPE	Pearson
BigSmall	PURE	PURE	72	10	0.68	1.547	0.98	0.981
BigSmall	PURE	PURE	72	20	0.117	0.454	0.163	0.999
BigSmall	PURE	PURE	72	30	0.176	0.556	0.333	0.998
BigSmall	PURE	PURE	72	5	1.598	3.568	2.529	0.914
BigSmall	PURE	UBFC	72	10	3.419	11.862	3.338	0.817
BigSmall	PURE	UBFC	72	20	3.999	13.953	3.533	0.725
BigSmall	PURE	UBFC	72	3	6.285	15.475	6.353	0.711
BigSmall	PURE	UBFC	72	30	5.323	15.329	4.851	0.69
BigSmall	PURE	UBFC	72	5	5.251	14.283	5.156	0.75
BigSmall	UBFC	PURE	72	10	5.819	18.685	5.468	0.636
BigSmall	UBFC	PURE	72	20	4.634	16.923	4.015	0.706
BigSmall	UBFC	PURE	72	3	9.238	19.944	10.24	0.501
BigSmall	UBFC	PURE	72	30	6.071	19.852	5.304	0.573
BigSmall	UBFC	PURE	72	5	7.516	19.226	8.346	0.603
BigSmall	UBFC	PURE	72	10	23.555	35.99	22.892	0.415
BigSmall	UBFC	PURE	72	5	23.547	35.466	24.815	0.33
BigSmall	UBFC	UBFC	72	10	0.586	1.435	0.538	0.994
BigSmall	UBFC	UBFC	72	20	2.539	4.184	2.43	0.947
BigSmall	UBFC	UBFC	72	30	0	0	0	1
BigSmall	UBFC	UBFC	72	5	0.721	2.252	0.712	0.979
BigSmall	UBFC	PURE	72	10	5.718	17.785	5.532	0.677
BigSmall	PURE	UBFC	72	10	3.291	11.376	3.186	0.825
DeepPhys	PURE	PURE	72	10	0.68	1.547	1.079	0.981
DeepPhys	PURE	PURE	72	20	0.117	0.454	0.163	0.999
DeepPhys	PURE	PURE	72	30	0.176	0.556	0.333	0.998
DeepPhys	PURE	PURE	72	5	1.004	2.658	1.511	0.949
DeepPhys	PURE	UBFC	72	10	1.855	7.763	1.904	0.913
DeepPhys	PURE	UBFC	72	20	1.516	5.287	1.557	0.957
DeepPhys	PURE	UBFC	72	3	4.646	12.756	4.812	0.778
DeepPhys	PURE	UBFC	72	30	1.684	5.988	1.745	0.949
DeepPhys	PURE	UBFC	72	5	2.609	9.021	2.647	0.884
DeepPhys	UBFC	PURE	72	10	5.635	17.641	6.076	0.674
DeepPhys	UBFC	PURE	72	20	4.896	17.153	4.673	0.701
DeepPhys	UBFC	PURE	72	3	7.857	17.698	9.472	0.627
DeepPhys	UBFC	PURE	72	30	3.662	13.585	3.588	0.819
DeepPhys	UBFC	PURE	72	5	7.111	17.926	8.497	0.663
DeepPhys	UBFC	PURE	72	10	26.719	39.369	26.05	0.178
DeepPhys	UBFC	PURE	72	20	25.195	39.839	22.811	0.019
DeepPhys	UBFC	PURE	72	5	23.027	33.922	24.852	0.392
DeepPhys	UBFC	UBFC	72	10	0.977	2.748	1.069	0.975
DeepPhys	UBFC	UBFC	72	20	2.148	3.262	2.04	0.965
DeepPhys	UBFC	UBFC	72	30	3.809	9.329	3.283	0.537
DeepPhys	UBFC	UBFC	72	5	0.721	2.252	0.722	0.981
DeepPhys	UBFC	UBFC	72	10	0.879	1.758	0.893	1
DeepPhys	UBFC	UBFC	72	20	0	0	0	1
DeepPhys	UBFC	UBFC	72	30	0	0	0	1
DeepPhys	UBFC	UBFC	72	5	4.688	11.951	4.663	0.775
EfficientPhys	PURE	PURE	72	10	0.567	1.412	0.94	0.991
EfficientPhys	PURE	PURE	72	20	0	0	0	1
EfficientPhys	PURE	PURE	72	30	0.176	0.556	0.333	0.999
EfficientPhys	PURE	PURE	72	5	0.974	2.616	1.474	0.969
EfficientPhys	PURE	UBFC	72	10	1.278	6.402	1.313	0.938
EfficientPhys	PURE	UBFC	72	20	1.376	5.991	1.373	0.942
EfficientPhys	PURE	UBFC	72	3	4.344	12.343	4.412	0.792
EfficientPhys	PURE	UBFC	72	30	1.43	5.837	1.395	0.942
EfficientPhys	PURE	UBFC	72	5	2.208	8.455	2.197	0.892
EfficientPhys	UBFC	PURE	72	10	3.33	12.931	3.543	0.834
EfficientPhys	UBFC	PURE	72	20	2.49	11.287	2.514	0.873
EfficientPhys	UBFC	PURE	72	3	8.358	18.714	10.177	0.566
EfficientPhys	UBFC	PURE	72	30	1.743	8.45	2.02	0.93
EfficientPhys	UBFC	PURE	72	5	5.794	15.515	7.061	0.748
EfficientPhys	UBFC	PURE	72	10	13.887	23.307	14.522	0.746
EfficientPhys	UBFC	PURE	72	20	15.625	28.416	14.746	0.633
EfficientPhys	UBFC	PURE	72	5	15.044	26.045	15.182	0.668
EfficientPhys	UBFC	UBFC	72	10	0.586	2.269	0.675	0.979
EfficientPhys	UBFC	UBFC	72	20	2.197	3.479	2.035	0.95
EfficientPhys	UBFC	UBFC	72	30	3.516	8.292	3.048	0.536
EfficientPhys	UBFC	UBFC	72	5	0.27	1.379	0.268	0.99
TSCAN	PURE	PURE	72	10	0.68	1.547	1.079	0.981
TSCAN	PURE	PURE	72	20	0.117	0.454	0.163	0.999
TSCAN	PURE	PURE	72	30	0.176	0.556	0.333	0.998
TSCAN	PURE	PURE	72	5	0.959	2.596	1.48	0.954
TSCAN	PURE	UBFC	72	10	2.296	9.068	2.315	0.884
TSCAN	PURE	UBFC	72	20	1.435	5.3	1.44	0.956
TSCAN	PURE	UBFC	72	3	4.424	12.432	4.623	0.796
TSCAN	PURE	UBFC	72	30	1.634	6.089	1.488	0.942
TSCAN	PURE	UBFC	72	5	2.388	8.85	2.467	0.89
TSCAN	UBFC	PURE	72	10	3	12.098	3.286	0.859
TSCAN	UBFC	PURE	72	20	3.249	12.525	3.265	0.846
TSCAN	UBFC	PURE	72	3	8.232	18.453	9.62	0.588
TSCAN	UBFC	PURE	72	30	1.628	7.435	1.924	0.948
TSCAN	UBFC	PURE	72	5	5.093	14.907	6.069	0.777
TSCAN	UBFC	PURE	72	10	24.609	37.156	24.768	0.366
TSCAN	UBFC	PURE	72	20	24.805	38.792	21.923	0.417
TSCAN	UBFC	PURE	72	5	22.075	34.563	22.586	0.364
TSCAN	UBFC	UBFC	72	10	1.367	3.612	1.48	0.955
TSCAN	UBFC	UBFC	72	20	2.148	3.262	2.04	0.965
TSCAN	UBFC	UBFC	72	30	4.688	9.574	4.064	0.513
TSCAN	UBFC	UBFC	72	5	0.361	1.592	0.368	0.989
TSCAN	UBFC	UBFC	72	10	0	0	0	1
TSCAN	UBFC	UBFC	72	20	0	0	0	1
TSCAN	UBFC	UBFC	72	5	4.922	13.525	4.911	0.763

• CNIBP

Bench Mark Git

Community

Feel free to contact us with any questions and suggestions. We welcome your contributions and cooperation.

Please feel free to contact us and join Slack.

Contacts

• Dae Yeol Kim, spicyyeol@gmail.com
• Kwangkee Lee, kwangkeelee@gmail.com

Funding

This work was partly supported by the ICT R&D program of MSIP/IITP. [2021(2021-0-00900), Adaptive Federated Learning in Dynamic Heterogeneous Environment]