scholarly journals The usage of a hydraulic model of the vascular system in the development of verification units for blood pressure measuring devices

2012 ◽  
Vol 93 (2) ◽  
pp. 380-382
Author(s):  
A I Soyko ◽  
R N Karataev ◽  
I V Klyushkin ◽  
V A Gogin
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Blood Vessels ◽  
Vascular System ◽  
Circulatory System ◽  
Hydraulic Model ◽  
Rate Measurement ◽  
Measuring Devices ◽  
Heart Rate Measurement ◽  
Human Circulatory System

The hydraulic model of the human circulatory system was discussed, presented from the position of classification and systematization of the major blood vessels, identified were the main consumers of the circulatory system, considered in detail was the area of regulation associated with the processes of blood pressure and heart rate measurement.

scholarly journals Changes in Blood Pressure and Heart Rate Measurement Undergraduate Students During Exam Period

2017 ◽  
Vol 08 (02) ◽  
Author(s):  
Senol Dogan ◽  
Nilay Nalcaci ◽  
Serkan Dogan ◽  
Almir Badnjevic ◽  
Amina Kurtovic ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Undergraduate Students ◽  
Rate Measurement ◽  
Heart Rate Measurement

scholarly journals Assessment of Manual Blood Pressure and Heart Rate Measurement Skills of Pharmacy Students: A Follow-Up Investigation

2008 ◽  
Vol 72 (3) ◽  
pp. 60 ◽  
Author(s):  
Katherine E. Elliott ◽  
Kenneth L. McCall ◽  
David S. Fike ◽  
Jill Polk ◽  
Cynthia Raehl
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Pharmacy Students ◽  
Rate Measurement ◽  
Heart Rate Measurement ◽  
Manual Blood Pressure

scholarly journals A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3719
Author(s):  
Aoxin Ni ◽  
Arian Azarang ◽  
Nasser Kehtarnavaz
Keyword(s):  
Heart Rate ◽  
Deep Learning ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Video Camera ◽  
Measurement Methods ◽  
Rate Measurement ◽  
Learning Methods ◽  
The Public ◽  
Heart Rate Measurement

The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of conventional contactless methods for heart rate measurement. After providing a review of the related literature, a comparison of the deep learning methods whose codes are publicly available is conducted in this paper. The public domain UBFC dataset is used to compare the performance of these deep learning methods for heart rate measurement. The results obtained show that the deep learning method PhysNet generates the best heart rate measurement outcome among these methods, with a mean absolute error value of 2.57 beats per minute and a mean square error value of 7.56 beats per minute.

scholarly journals Heart Rate Measurement

2021 ◽  
Vol 1831 (1) ◽  
pp. 012020
Author(s):  
Parth Kansara ◽  
Ritwik Dhar ◽  
Riddhi Shah ◽  
Devansh Mehta ◽  
Purva Raut
Keyword(s):  
Heart Rate ◽  
Rate Measurement ◽  
Heart Rate Measurement
Sign in / Sign up

Export Citation Format

Share Document