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Conference Paper
LSTC-rPPG: Long Short-Term Convolutional Network for Remote Photoplethysmography
- Authors
- Jun Seong Lee, Gyutae Hwang, Moonwook Ryu, Sang Jun Lee
- Issue Date
- 2023-06
- Citation
- Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2023, pp.6014-6022
- Language
- English
- Type
- Conference Paper
- Abstract
- Remote photoplethysmography (rPPG) is a non-contact technique for measuring blood pulse signals associated with cardiac activity. Although rPPG is considered an alternative to traditional contact-based photoplethysmography (PPG) because of its non-contact nature, obtaining reliable measurements remains a challenge owing to the sensitiveness of rPPG. In recent years, deep learning-based methods have improved the reliability of rPPG, but they suffer from certain limitations in utilizing long-term features such as periodic tendencies over long durations. In this paper, we propose a deep learning-based method that models long short-term spatio-temporal features and optimizes the long short-term features, ensuring reliable rPPG. The proposed method is composed of three key components: i) a deep learning architecture, denoted by LSTC-rPPG, which models long short-term spatio-temporal features and combines the features for reliable rPPG, ii) a temporal attention refinement module that mitigates temporal mismatches between the long-term and short-term features, and iii) a frequency scale invariant hybrid loss to guide long-short term features. In experiments on the UBFC-rPPG database, the proposed method demonstrated a mean absolute error of 0.7, root mean square error of 1.0, and Pearson correlation coefficient of 0.99 for heart rate estimation accuracy, outperforming contemporary state-of-the-art methods.
- KSP Keywords
- Cardiac activity, Convolutional networks, Estimation accuracy, Heart rate estimation, Key Components, Long-short, Mean Absolute Error, Non-contact, Pearson correlation coefficient, Reliable measurements, Root mean square(RMS)