Non-contact Facial based Vital Sign Estimation using Convolutional Neural Network Approach

A rapid heart rate may indicate early diagnosis of heart disease, which could result in abrupt mortality if a heart attack occurs while exercising. A fatal incident is usually precipitated by a heart attack while strenuously exercising. This paper proposed invasive health monitoring through remote photoplethysmography (rPPG) analysis captured by RGB video camera to measure a wide range of biological data. A non-contact facial-based vital signs prediction can facilitate checking pulse rate and respiration rate regularly. Several studies have been conducted on evaluating rPPG signals under a variety of static conditions and little head movement, including different skin tones, angles of the camera, and distance from the camera. A study of heart rate (HR) and breathing rate (BR) data from facial videos for fitness applications were presented in this paper. Most studies still do not have a way to measure vital sign estimation especially for physical activity application from facial videos. The face detector was applied based on three regions of interest on facial landmarks for vital sign estimation. Then, the rPPG method with convolutional neural network (CNN) is presented to construct a spatio-temporal mapping of essential characteristics and estimate the vital sign from a sequence of facial images of people after doing various types of exercises. This will allow people to keep track of their health while exercising and creating a tailored training program based on their physiological preferences. The absolute error (AE) between the estimated HR and the reference HR from all experiments is 2.16 ± 2.2 beats/min. While the AE for the estimated BR from the references BR are 1.53 ± 2.3 beats/min.

@article{Suriani2022,
  title     = {Non-contact Facial based Vital Sign Estimation using Convolutional Neural Network Approach},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {5},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Nor Surayahani Suriani and Nur Syahida Shahdan and Nan Md. Sahar and Nik Shahidah Afifi Md. Taujuddin},
  doi       = {10.14569/IJACSA.2022.0130546},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130546}
}