A Supervised Learning Approach for Robust Health Monitoring using Face
Videos
- URL: http://arxiv.org/abs/2102.00322v1
- Date: Sat, 30 Jan 2021 22:03:16 GMT
- Title: A Supervised Learning Approach for Robust Health Monitoring using Face
Videos
- Authors: Mayank Gupta and Lingjun Chen and Denny Yu and Vaneet Aggarwal
- Abstract summary: Non-contact, device-free human sensing methods can eliminate the need for specialized heart and blood pressure monitoring equipment.
In this paper, we used a non-contact method that only requires face videos recorded using commercially-available webcams.
The proposed approach used facial recognition to detect the face in each frame of the video using facial landmarks.
- Score: 32.157163136267954
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Monitoring of cardiovascular activity is highly desired and can enable novel
applications in diagnosing potential cardiovascular diseases and maintaining an
individual's well-being. Currently, such vital signs are measured using
intrusive contact devices such as an electrocardiogram (ECG), chest straps, and
pulse oximeters that require the patient or the health provider to manually
implement. Non-contact, device-free human sensing methods can eliminate the
need for specialized heart and blood pressure monitoring equipment. Non-contact
methods can have additional advantages since they are scalable with any
environment where video can be captured, can be used for continuous
measurements, and can be used on patients with varying levels of dexterity and
independence, from people with physical impairments to infants (e.g., baby
camera). In this paper, we used a non-contact method that only requires face
videos recorded using commercially-available webcams. These videos were
exploited to predict the health attributes like pulse rate and variance in
pulse rate. The proposed approach used facial recognition to detect the face in
each frame of the video using facial landmarks, followed by supervised learning
using deep neural networks to train the machine learning model. The videos
captured subjects performing different physical activities that result in
varying cardiovascular responses. The proposed method did not require training
data from every individual and thus the prediction can be obtained for the new
individuals for which there is no prior data; critical in approach
generalization. The approach was also evaluated on a dataset of people with
different ethnicity. The proposed approach had less than a 4.6\% error in
predicting the pulse rate.
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