International Conference on Knowledge Discovery and Data Mining (KDD) 2023, pp.1-4
Language
English
Type
Conference Paper
Project Code
23HR4200, Development of Collective Collaboration Intelligence Framework for Internet of Autonomous Things,
Sunhwan Lim
Abstract
In pre-clinical health monitoring, estimating physiological signals from video is a low-cost and convenient tool. Remote photoplethysmography (rPPG) involves placing a camera in a remote area to estimate a person's heart rate or Blood Volume Pulse (BVP). In this paper, we propose an attention based deep architecture for rPPG estimation that assimilate temporal relationship across a sequence of frames while focusing on the relevant features and regions by exploiting the inter-pixel relationship of feature maps. Also, we design a dynamic supervision strategy using frequency and time domain losses to mitigate overfitting for efficient estimation of rPPG signals. The proposed method was evaluated on two publicly available rPPG datasets (UBFC-rPPG and PURE). The findings of this study demonstrate that promising results can be achieved by enforcing an adequate balance between time-frequency supervision.
KSP Keywords
Deep architecture, Facial video, Feature Map, Health monitoring, Heart rate, Inter-pixel, Low-cost, Physiological signals, Pre-clinical, Remote Areas, Temporal relationship
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