Video-based human heart rate measurement using joint blind source separation

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.

Download Full-text

Heart Rate Measurement

Journal of Physics Conference Series ◽

10.1088/1742-6596/1831/1/012020 ◽

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

Download Full-text

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

IEEE Access ◽

10.1109/access.2019.2950423 ◽

2019 ◽

Vol 7 ◽

pp. 158492-158502 ◽

Cited By ~ 5

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

Download Full-text

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

Metrology and Measurement Systems ◽

10.1515/mms-2016-0052 ◽

2016 ◽

Vol 23 (4) ◽

pp. 579-592 ◽

Cited By ~ 14

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.

Download Full-text