-
DeepPhys : DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks
-
MTTS :Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement
- need to verification
-
DeepPhys + LSTM
-
PP-Net: A Deep Learning Framework for PPG based Blood Pressure and Heart Rate Estimation
- before test modify sample2.cfg(./pyVHR/analysis/sample2.cfg)
[DEFAULT]
'''
methods = ['POS','CHROM','ICA','SSR','LGI','PBV','GREEN'] # Change Method
'''
[VIDEO]
dataset = LGI_PPGI # change dataset
videodataDIR= /media/hdd1/LGGI/ # change dataset path
BVPdataDIR = /media/hdd1/LGGI/
;videoIdx = all
videoIdx = [1,2,5,6] # change test video idx
detector = media-pipe # use media-pipe, it's proposed ROI option
- before test, modify test suit file(./pyVHR/analysis/testsuite.py), all regions one-hot mapping.
'''
test for all region
'''
# tmp = bin(test)
# binary = ''
# for i in range(mask_num-len(tmp[2:])):
# binary += '0'
# binary += tmp[2:]
'''
test for top-5 & bot -5
'''
if test_case == 0 :
binary = '0011000000000000000100000001001'
else :
binary = '0000000001100001011000000000000'
-
run _1_rppg_assesment.py
-
all mask information found at video.py's make_mask function (./pyVHR/signals/video.py)
Dae Yeol Kim, spicyyeol@gmail.com
Jin Soo Kim, wlstn25092303@tvstorm.com
Kwangkee Lee, kwangkeelee@gmail.com
min a Lee, mina1505@kw.ac.kr
Jun Yeong Na, najubae@kw.ac.kr
JongEui Chae, forownsake@gmail.com
This work was supported by the ICT R&D program of MSIP/IITP. [2021(2021-0-00900), Adaptive Federated Learning in Dynamic Heterogeneous Environment]