A Supervised Learning Approach for Robust Health Monitoring using Face Videos

@article{Gupta2021ASL,
  title={A Supervised Learning Approach for Robust Health Monitoring using Face Videos},
  author={Mayank Gupta and Lingjun Chen and Denny Yu and Vaneet Aggarwal},
  journal={Proceedings of the 2nd ACM Workshop on Device-Free Human Sensing},
  year={2021}
}
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. User engagement and compliance with wearables is a well-known problem that presents a significant… 
1 Citations

Figures from this paper

References

SHOWING 1-10 OF 28 REFERENCES
DistancePPG: Robust non-contact vital signs monitoring using a camera
Vital signs such as pulse rate and breathing rate are currently measured using contact probes. But, non-contact methods for measuring vital signs are desirable both in hospital settings (e.g. in
Robust Heart Rate Measurement from Video Using Select Random Patches
  • Antony Lam, Y. Kuno
  • Computer Science
    2015 IEEE International Conference on Computer Vision (ICCV)
  • 2015
TLDR
This paper presents conditions under which cardiac activity extraction from local regions of the face can be treated as a linear Blind Source Separation problem and proposes a simple but robust algorithm for selecting good local regions.
Motion-compensated noncontact imaging photoplethysmography to monitor cardiorespiratory status during exercise.
TLDR
Continuous recordings from the iPPG system reveal that heart and respiration rates can be successfully tracked with the artifact reduction method even in high-intensity physical exercise situations, leading to a new avenue for noncontact sensing of vital signs and remote physiological assessment, with clear applications in triage and sports training.
Remote Heart Rate Measurement from Face Videos under Realistic Situations
TLDR
A framework which utilizes face tracking and Normalized Least Mean Square adaptive filtering methods to counter their influences is proposed and it is demonstrated that this method substantially outperforms all previous methods.
Deep learning with time-frequency representation for pulse estimation from facial videos
TLDR
This work has endeavored to develop a novel deep learning approach as the core part for pulse (heart rate) estimation by using a common RGB camera, and developed a pulse database, called the Pulse from Face, and used it to train the CNN.
Heartbeat Rate Measurement from Facial Video
TLDR
The proposed method uses a facial feature point-tracking method that combines a good feature to track method with a supervised descent method to overcome the limitations of currently available facial video-based HR measuring systems.
Supervised learning approach to remote heart rate estimation from facial videos
TLDR
A supervised machine learning approach to remote video-based heart rate (HR) estimation is proposed and the algorithm was evaluated against the state-of-the-art on 120 minutes of face videos, the largest video- based heart rate evaluation to date.
Advancements in Noncontact, Multiparameter Physiological Measurements Using a Webcam
We present a simple, low-cost method for measuring multiple physiological parameters using a basic webcam. By applying independent component analysis on the color channels in video recordings, we
Robust Pulse Rate From Chrominance-Based rPPG
  • G. Haan, V. Jeanne
  • Computer Science, Medicine
    IEEE Transactions on Biomedical Engineering
  • 2013
TLDR
This work presents an analysis of the motion problem, from which far superior chrominance-based methods emerge, and shows remote photoplethysmography methods to perform in 92% good agreement with contact PPG, with RMSE and standard deviation both a factor of 2 better than BSS- based methods.
The ear as an alternative site for a pulse oximeter finger clip sensor.
TLDR
A pulse oximeter finger clip sensor placed on the ear does not provide clinically reliable S(pO2) readings, according to this prospective study with patients undergoing pulmonary function testing.
...
1
2
3
...