Key result
Remote monitoring of cardiorespiratory signals from a hovering UAV using CEEMDAN and CCA achieved highly accurate readings, with a heart rate RMSE of 0.31 beats/min in stationary subjects, outperforming existing methods.
Why the study?
Does remote video-photoplethysmography from a hovering UAV accurately measure heart rate and respiratory rate in healthy volunteers compared to standard contact sensors?
Does remote video-photoplethysmography from a hovering UAV accurately measure heart rate and respiratory rate in healthy volunteers compared to standard contact sensors?
Answers are grounded in this paper and cite their sources.
Remote monitoring of vital signs using video from a hovering UAV is feasible and highly accurate compared to standard contact sensors, offering potential for non-contact triage and monitoring.
BACKGROUND: Remote physiological measurement might be very useful for biomedical diagnostics and monitoring. This study presents an efficient method for remotely measuring heart rate and respiratory rate from video captured by a hovering unmanned aerial vehicle (UVA). The proposed method estimates heart rate and respiratory rate based on the acquired signals obtained from video-photoplethysmography that are synchronous with cardiorespiratory activity. METHODS: Since the PPG signal is highly affected by the noise variations (illumination variations, subject's motions and camera movement), we have used advanced signal processing techniques, including complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and canonical correlation analysis (CCA) to remove noise under these assumptions. RESULTS: To evaluate the performance and effectiveness of the proposed method, a set of experiments were performed on 15 healthy volunteers in a front-facing position involving motion resulting from both the subject and the UAV under different scenarios and different lighting conditions. CONCLUSION: The experimental results demonstrated that the proposed system with and without the magnification process achieves robust and accurate readings and have significant correlations compared to a standard pulse oximeter and Piezo respiratory belt. Also, the squared correlation coefficient, root mean square error, and mean error rate yielded by the proposed method with and without the magnification process were significantly better than the state-of-the-art methodologies, including independent component analysis (ICA) and principal component analysis (PCA).
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Al‐Naji et al. (2017) studied Healthy (n=15). Remote video-photoplethysmography from a hovering UAV using CEEMDAN and CCA vs. Standard pulse oximeter, Piezo respiratory belt, ICA, and PCA was evaluated on Heart rate and respiratory rate measurement accuracy (RMSE and squared correlation coefficient). Remote monitoring of cardiorespiratory signals from a hovering UAV using CEEMDAN and CCA achieved highly accurate readings, with a heart rate RMSE of 0.31 beats/min in stationary subjects, outperforming existing methods.
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