원격 PPG를 위한 잡음에 강인한 심박수 추정 알고리즘
Noise-robust Heart Rate Estimation Algorithm for Remote Photoplethysmography
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초록
This paper proposes a robust algorithm for heart rate estimation using remote photoplethysmography (rPPG). The algorithm employs a combination of adaptive filtering and frequency tracking to enhance the signal-to-noise ratio (SNR) and accurately estimate heart rates from facial videos. The LGI dataset, comprising videos of six participants performing various activities (resting, rotation, talk, gym), was utilized for evaluation. The ground truth heart rate was obtained using a CMS50E pulse oximeter, and a 10-second data window with FFT-based frequency analysis was applied to derive reference heart rates. The proposed method detects the face using Mediapipe API, selects the forehead region of interest (ROI), and extracts RGB signals. The signals undergo preprocessing, motion noise removal via adaptive filtering, and heart rate estimation using an adaptive notch filter. Experimental results demonstrate that the proposed algorithm outperforms existing methods, especially in challenging conditions such as during gym and talk activities.
Abstract
This paper proposes a robust algorithm for heart rate estimation using remote photoplethysmography (rPPG). The algorithm employs a combination of adaptive filtering and frequency tracking to enhance the signal-to-noise ratio (SNR) and accurately estimate heart rates from facial videos. The LGI dataset, comprising videos of six participants performing various activities (resting, rotation, talk, gym), was utilized for evaluation. The ground truth heart rate was obtained using a CMS50E pulse oximeter, and a 10-second data window with FFT-based frequency analysis was applied to derive reference heart rates. The proposed method detects the face using Mediapipe API, selects the forehead region of interest (ROI), and extracts RGB signals. The signals undergo preprocessing, motion noise removal via adaptive filtering, and heart rate estimation using an adaptive notch filter. Experimental results demonstrate that the proposed algorithm outperforms existing methods, especially in challenging conditions such as during gym and talk activities.
참고문헌 (20)
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