Key result
Multi-level constrained spatiotemporal representation cuts BVP signal measurement RMSE ~40% versus state-of-the-art methods.
Why the study?
Facial video-based blood volume pulse measurement holds potential for remote health monitoring, but existing methods have issues with convolutional kernel perceptual field constraints.
Does an end-to-end multi-level constrained spatiotemporal representation structure improve the accuracy of facial video-based BVP signal measurement compared to existing methods?
Does an end-to-end multi-level constrained spatiotemporal representation structure improve the accuracy of facial video-based BVP signal measurement compared to existing methods?
A novel multi-level constrained spatiotemporal representation structure significantly improves the accuracy of facial video-based blood volume pulse measurement, offering potential for non-contact heart health monitoring.
Facial video-based blood volume pulse (BVP) signal measurement holds great potential for remote health monitoring, while existing methods have issues with convolutional kernel perceptual field constraints. This article proposes an end-to-end multi-level constrained spatiotemporal representation structure for facial video-based BVP signal measurement. First, an intra- and inter-subject feature representation is proposed to strengthen the BVP-related features generation at high, semantic, and shallow levels, respectively. Second, the global-local association is presented to enhance BVP signal period pattern learning, and the global temporal features are introduced into the local spatial convolution of each frame by adaptive kernel weights. Finally, the multi-dimensional fused features are mapped to one-dimensional BVP signals by the task-oriented signal estimator. The experimental results on the publicly available MMSE-HR dataset demonstrate that the proposed structure overperforms state-of-the-art methods (e.g., AutoHR) in BVP signal measurement, with a 20% and 40% reduction in mean absolute error and root mean squared error, respectively. The proposed structure would be a powerful tool for telemedical and non-contact heart health monitoring.
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Li et al. (2023) studied Blood volume pulse (BVP) signal measurement. Multi-level constrained spatiotemporal representation structure vs. State-of-the-art methods (e.g., AutoHR) was evaluated on Mean absolute error and root mean squared error in BVP signal measurement (20% reduction in MAE, 40% reduction in RMSE). The proposed multi-level constrained spatiotemporal representation structure outperformed state-of-the-art methods like AutoHR, reducing mean absolute error by 20% and root mean squared error by 40%.
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