Physiological signals, including heart rate (HR), heart rate variability (HRV), and respiratory frequency (RF) are important indicators of our health, which are usually measured in clinical examinations. Traditional physiological signal measurement often involves contact sensors, which may be inconvenient or cause discomfort in long-term monitoring sessions. Recently, there were studies exploring remote HR measurement from facial videos, and several methods have been proposed. However, previous methods cannot be fairly compared, since they mostly used private, self-collected small datasets as there has been no public benchmark database for the evaluation. Besides, we haven’t found any study that validates such methods for clinical applications yet, e.g., diagnosing cardiac arrhythmias/disease, which could be one major goal of this technology. In this paper, we introduce the Oulu Bio-Face (OBF) database as a benchmark set to fill in the blank. The OBF database includes large number of facial videos with simultaneously recorded reference physiological signals. The data were recorded both from healthy subjects and from patients with atrial fibrillation (AF), which is the most common sustained and widespread cardiac arrhythmia encountered in clinical practice. Accuracy of HR, HRV and RF measured from OBF videos are provided as the baseline results for future evaluation. We also demonstrated that the video-extracted HRV features can achieve promising performance for AF detection, which has never been studied before. From a wider outlook, the remote technology may lead to convenient self-examination in mobile condition for earlier diagnosis of the arrhythmia.
Li Xiaobai, Alikhani Iman, Shi Jingang, Seppänen Tapio, Junttila Juhani, Majamaa-Voltti Kirsi, Tulppo Mikko, Zhao Guoying
A4 Article in conference proceedings
Place of publication:
13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018
X. Li et al., “The OBF Database: A Large Face Video Database for Remote Physiological Signal Measurement and Atrial Fibrillation Detection,” 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), Xi’an, 2018, pp. 242-249. doi: 10.1109/FG.2018.00043
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