ReViSe: Remote Vital Signs Measurement Using Smartphone Camera
- URL: http://arxiv.org/abs/2206.08748v1
- Date: Mon, 13 Jun 2022 19:20:11 GMT
- Title: ReViSe: Remote Vital Signs Measurement Using Smartphone Camera
- Authors: Donghao Qiao, Amtul Haq Ayesha, Farhana Zulkernine, Raihan Masroor,
Nauman Jaffar
- Abstract summary: Remote Photoplethysmography (rVi) is a fast, effective, inexpensive and convenient method for collecting biometric data.
We propose an end-to-end framework to measure people's vital signs based on the ruration of a user's face captured with a smartphone camera.
We extract face landmarks with a deep learning-based neural network model in real-time.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Remote Photoplethysmography (rPPG) is a fast, effective, inexpensive and
convenient method for collecting biometric data as it enables vital signs
estimation using face videos. Remote contactless medical service provisioning
has proven to be a dire necessity during the COVID-19 pandemic. We propose an
end-to-end framework to measure people's vital signs including Heart Rate (HR),
Heart Rate Variability (HRV), Oxygen Saturation (SpO2) and Blood Pressure (BP)
based on the rPPG methodology from the video of a user's face captured with a
smartphone camera. We extract face landmarks with a deep learning-based neural
network model in real-time. Multiple face patches also called
Region-of-Interests (RoIs) are extracted by using the predicted face landmarks.
Several filters are applied to reduce the noise from the RoIs in the extracted
cardiac signals called Blood Volume Pulse (BVP) signal. We trained and
validated machine learning models using two public rPPG datasets namely the
TokyoTech rPPG and the Pulse Rate Detection (PURE) datasets, on which our
models achieved the following Mean Absolute Errors (MAE): a) for HR, 1.73 and
3.95 Beats-Per-Minute (bpm) respectively, b) for HRV, 18.55 and 25.03 ms
respectively, and c) for SpO2, a MAE of 1.64 on the PURE dataset. We validated
our end-to-end rPPG framework, ReViSe, in real life environment, and thereby
created the Video-HR dataset. Our HR estimation model achieved a MAE of 2.49
bpm on this dataset. Since no publicly available rPPG datasets existed for BP
measurement with face videos, we used a dataset with signals from fingertip
sensor to train our model and also created our own video dataset, Video-BP. On
our Video-BP dataset, our BP estimation model achieved a MAE of 6.7 mmHg for
Systolic Blood Pressure (SBP), and a MAE of 9.6 mmHg for Diastolic Blood
Pressure (DBP).
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