Physics-Constrained Deep Learning Model for Contactless Blood Pressure Monitoring from Triaxial Bodyseismography
Ballistocardiography (BCG) is promising for unobtrusive long-term blood pressure (BP) monitoring in laboratory settings, but traditional BCG signals are vulnerable to the variations in body-bed interaction with shifted fiducial points in temporal or amplitude axis, and BP varies with personal hemodynamic changes, causing misaligned representations that affect model generalizability and robustness. In this work, we propose a non-invasive BP estimation framework, Phy-BP, based on triaxial bodyseismography (BSG) as an extension of BCG. Firstly, an adaptive quality-control algorithm is designed to
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- PossiblePossibly related (embedding) · 60%The role of dataset integrity, calibration and signal quality in neural network-based blood pressure estimation - Nature →
- PossiblePossibly related (embedding) · 52%Wearable sensors and AI could monitor blood pressure in ICU - Johns Hopkins University →
- FuzzySimilar title/name (fuzzy) · 87%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.94): “Physics-Constrained Deep Learning Model for Contactless Bloo” ≈ “aymericdamien/TopDeepLearning””
- FuzzySimilar title/name (fuzzy) · 66%kmario23/deep-learning-drizzle →
“Fuzzy title match (0.78): “Physics-Constrained Deep Learning Model for Contactless Bloo” ≈ “kmario23/deep-learning-drizzle””
- FuzzyOverlapping authors or contributors · 62%rasbt/LLMs-from-scratch →
“Shared author/contributor keys: yin”
- LinkedLinked via arxiv author · 85%Yuanyuan Zhang →
“Physics-Constrained Deep Learning Model for Contactless Blood Pressure Monitoring from Triaxial Bodyseismography”
- LinkedLinked via arxiv author · 85%Yida Zhang →
“Physics-Constrained Deep Learning Model for Contactless Blood Pressure Monitoring from Triaxial Bodyseismography”
- LinkedLinked via arxiv author · 85%Jiahui Li →
“Physics-Constrained Deep Learning Model for Contactless Blood Pressure Monitoring from Triaxial Bodyseismography”