Improving Accuracy of Contactless Respiratory Rate Estimation by Enhancing Thermal Sequences with Deep Neural Networks
Estimation of vital signs using image processing techniques have already been proved to have a potential for supporting remote medical diagnostics and replacing traditional measurements that usually require special hardware and electrodes placed on a body. In this paper, we further extend studies on...
Main Authors: | Alicja Kwasniewska, Jacek Ruminski, Maciej Szankin |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-10-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/9/20/4405 |
Similar Items
-
Remote Photoplethysmography Is an Accurate Method to Remotely Measure Respiratory Rate: A Hospital-Based Trial
by: Albuisson, E., et al.
Published: (2022) -
Validation of a New Contactless and Continuous Respiratory Rate Monitoring Device Based on Ultra-Wideband Radar Technology
by: Timo Lauteslager, et al.
Published: (2021-06-01) -
Remote Respiratory Monitoring in the Time of COVID-19
by: Carlo Massaroni, et al.
Published: (2020-05-01) -
Deep Residual Squeeze and Excitation Network for Remote Sensing Image Super-Resolution
by: Jun Gu, et al.
Published: (2019-08-01) -
Object Detection for Contactless Vital Signs Estimation
by: Yang, Fan
Published: (2021)