photoplethysmogram
PulseAugur coverage of photoplethysmogram — every cluster mentioning photoplethysmogram across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
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Machine Learning Explores Non-Invasive Blood Glucose Monitoring via Smartwatches
Researchers have explored the use of machine learning and deep learning to estimate blood glucose levels non-invasively using photoplethysmogram (PPG) signals from smartwatches. This approach aims to overcome the limita…
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Transformer model estimates blood pressure from PPG signals
Researchers have developed a new Transformer-based model called DMT for estimating blood pressure from photoplethysmography (PPG) signals without a cuff. The model incorporates demographic information through feature mo…
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Orbital Industries raises $50M for AI-driven materials discovery
Orbital Industries, a startup focused on using AI to discover and manufacture advanced materials, has secured $50 million in Series B funding. The round was led by Plural, with participation from Nvidia's venture arm an…
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VAMP-Diff model enhances realism in physiological signal generation
Researchers have developed VAMP-Diff, a novel variational diffusion model designed to generate more realistic photoplethysmography (PPG) signals. This model integrates a temporal PPG encoder with a conditional diffusion…
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New deep learning model estimates cardiac output from PPG signals
Researchers have developed a novel deep learning model called CVAF-Net for estimating cardiac output from short photoplethysmography (PPG) signals. This model processes both raw PPG data and a feature sequence map, fusi…
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New model synthesizes physiological signals with parameter efficiency
Researchers have developed a new parameter-efficient foundation model called Compact Latent Manifold Translation (CLMT) for synthesizing physiological signals. This model addresses challenges like modality and frequency…
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New AI model xMAE learns biosignal timing for better health predictions
Researchers have developed a new pretraining framework called xMAE designed to learn meaningful representations from biosignals. This method specifically addresses the temporal dynamics between different biosignals, suc…
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Deep learning models show promise in pavement, aero-engine, and affect recognition tasks
Researchers are exploring deep learning models for predictive maintenance and performance analysis across various domains. One study utilizes CNN and LSTM networks with extensive pavement condition data from Texas to mo…
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New generative self-supervised learning framework improves physiological estimation from PPG data
Researchers have developed a new generative self-supervised learning framework called TS2TC to improve the estimation of physiological parameters from photoplethysmography (PPG) data. This framework addresses the challe…
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New theory grounds cardiac health monitoring in smartphone photoplethysmography
Researchers have developed Cardiac Stability Theory (CST), a new framework that defines cardiovascular health based on stability margins around a cardiac dynamical attractor. This theory leads to the Cardiac Stability I…