JIITA, vol.5 no.1, p.397-400, 2021, DOI: 10.22664/ISITA.2021.5.1.397
Hyunduk Kim*, Sang-Heon Lee, Myoung-Kyu Sohn
Division of Automotive Technology, DGIST, Daegu, Republic of Korea
Abstract: Remote heart rate (rHR) estimation, which aims to measure heart activities without any physical contact with the subject, is performed using remote photoplethys- mography (rPPG) and has great potential in many applications. In this paper, we introduce a remote heart rate estimation algorithm to detect driver drowsiness. The proposed method consists of two parts: an rPPGNet for PPG signal prediction from input video frames and an rHRNet for heart rate estimation from predicted PPG signal. Moreover, we apply a skin-based attention module and partition constraint to estimate more accurate rHR. To evaluate the performance of the proposed network, we train and test the proposed network on three public datasets.
Keywords: remote heart rate; remote photoplethysmography; skin-based attention module; spatio-temporal convolution network; driver drowsiness detection
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