photoplethysmogram
PulseAugur coverage of photoplethysmogram — every cluster mentioning photoplethysmogram across labs, papers, and developer communities, ranked by signal.
7 day(s) with sentiment data
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Samsung AI models enable real-time smartwatch cardiac diagnostics
Samsung Research America has introduced two new AI models, xMAE and HiMAE, designed for on-device interpretation of electrocardiogram (ECG) and photoplethysmogram (PPG) data. These models enable advanced cardiac diagnos…
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Samsung unveils AI models for wearable health data analysis
Samsung Research America has developed two AI foundation models, xMAE and HiMAE, designed to analyze biosignal data from wearable devices. These models utilize self-supervised learning to extract insights from unlabeled…
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New ProtoMM Framework Enhances Self-Supervised Multimodal Biosignal Learning
Researchers have introduced ProtoMM, a new self-supervised learning framework designed to improve the modeling of multimodal time-series data, particularly in biosignals. Unlike existing methods that can overfit to easi…
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New AI frameworks enhance sleep staging accuracy using detailed signal analysis · 2 sources tracked
Researchers are developing advanced methods for automated sleep staging, moving beyond traditional 30-second epoch analysis. One approach utilizes Hidden Semi-Markov Models to convert coarse epoch labels into second-lev…
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PPG blood glucose models fail real-world tests, study finds
A new evaluation pipeline for non-invasive blood glucose level estimation using photoplethysmography (PPG) has revealed significant overestimation of model performance. When tested with stricter, participant-aware proto…
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Cardiologent: Multi-agent AI system aids arrhythmia assessment and management
Researchers have developed Cardiologent, a multi-agent clinical decision support system designed to assess patient-level arrhythmia, urgency, and management. Unlike previous systems that focus on single ECG readings or …
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New AI model screens aortic valve disease using PPG signals
Researchers have developed a novel Physiology-Guided Self-Supervised Learning (PG-SSL) method to screen for aortic valve disease (AVD) using photoplethysmography (PPG) signals. This approach leverages approximately 170,…
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New research suggests direct PPG signals are superior for wearable blood pressure monitoring
Two new research papers explore methods for estimating blood pressure using wearable sensors, focusing on photoplethysmography (PPG) and electrocardiography (ECG) signals. The first paper proposes a lightweight hybrid l…
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Bio-Tuning Glasses: Invisible Biofeedback Interface Adapts Environment Using Edge AI
Researchers are developing Bio-Tuning Glasses, an experimental concept for an invisible biofeedback interface that adapts the user's environment based on their physiological state. Unlike typical wearables that notify u…
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Google Research unveils SensorFM, a foundation model for wearable health data · 4 sources tracked
Google Research has introduced SensorFM, a large foundation model for wearable health data. Pre-trained on over a trillion minutes of sensor data from five million individuals, SensorFM learns a general representation o…
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New framework TimeSynth benchmarks health signal forecasting models
Researchers have introduced TimeSynth, a new framework designed to benchmark forecasting models for health-signal digital twins. This framework addresses the limitations of current pointwise metrics, which fail to detec…
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New PCHS Framework Enhances Heart and Respiratory Rate Estimation from Wrist PPG
Researchers have developed a new framework called Physically-Constrained Harmonic Separation (PCHS) to improve the accuracy of heart rate (HR) and respiratory rate (RR) estimation from wrist-worn photoplethysmography (P…
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New PCHS Framework Improves Heart Rate Estimation from Wrist PPG
Researchers have developed a new framework called Physically-Constrained Harmonic Separation (PCHS) to improve the accuracy of heart rate (HR) and respiratory rate (RR) estimation from wrist-worn photoplethysmography (P…
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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…