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- Title
A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods.
- Authors
Ni, Aoxin; Azarang, Arian; Kehtarnavaz, Nasser
- Abstract
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.
- Subjects
HEART beat; DEEP learning; CAMCORDERS; IMAGE processing; ABSOLUTE value
- Publication
Sensors (14248220), 2021, Vol 21, Issue 11, p3719
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s21113719