On the target area tracking method for heart rate measurement using deep learning strategy

2019 ◽  
Author(s):  
Jiancheng Zou ◽  
Sai Zhang ◽  
Bailin Ge
Keyword(s):  
Heart Rate ◽  
Deep Learning ◽  
Learning Strategy ◽  
Target Area ◽  
Rate Measurement ◽  
Tracking Method ◽  
Heart Rate Measurement

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

scholarly journals Noncontact Heart Rate Measurement Based on an Improved Convolutional Sparse Coding Method Using IR-UWB Radar

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 158492-158502 ◽  
Author(s):  
Pengfei Wang ◽  
Fugui Qi ◽  
Miao Liu ◽  
Fulai Liang ◽  
Huijun Xue ◽  
...  
Keyword(s):  
Heart Rate ◽  
Sparse Coding ◽  
Rate Measurement ◽  
Uwb Radar ◽  
Heart Rate Measurement ◽  
Coding Method

scholarly journals Distant Measurement of Plethysmographic Signal in Various Lighting Conditions Using Configurable Frame-Rate Camera

2016 ◽  
Vol 23 (4) ◽  
pp. 579-592 ◽  
Author(s):  
Jaromir Przybyło ◽  
Eliasz Kańtoch ◽  
Mirosław Jabłoński ◽  
Piotr Augustyniak
Keyword(s):  
Heart Rate ◽  
Assisted Living ◽  
Detection Algorithm ◽  
Frame Rate ◽  
Infrared Light ◽  
Fluorescent Light ◽  
Detection Accuracy ◽  
Rate Measurement ◽  
Lighting Conditions ◽  
Heart Rate Measurement

Abstract Videoplethysmography is currently recognized as a promising noninvasive heart rate measurement method advantageous for ubiquitous monitoring of humans in natural living conditions. Although the method is considered for application in several areas including telemedicine, sports and assisted living, its dependence on lighting conditions and camera performance is still not investigated enough. In this paper we report on research of various image acquisition aspects including the lighting spectrum, frame rate and compression. In the experimental part, we recorded five video sequences in various lighting conditions (fluorescent artificial light, dim daylight, infrared light, incandescent light bulb) using a programmable frame rate camera and a pulse oximeter as the reference. For a video sequence-based heart rate measurement we implemented a pulse detection algorithm based on the power spectral density, estimated using Welch’s technique. The results showed that lighting conditions and selected video camera settings including compression and the sampling frequency influence the heart rate detection accuracy. The average heart rate error also varies from 0.35 beats per minute (bpm) for fluorescent light to 6.6 bpm for dim daylight.

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