AIMC Topic: Monitoring, Physiologic

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Design and analysis of TwinCardio framework to detect and monitor cardiovascular diseases using digital twin and deep neural network.

Scientific reports
World Health Organization (WHO) estimates 17.9 million deaths globally every year due to Cardiovascular Disease or CVD, which includes an array of disorders of the heart and blood vessels, that includes coronary heart disease, cerebrovascular disease...

Fused federated learning framework for secure and decentralized patient monitoring in healthcare 5.0 using IoMT.

Scientific reports
Federated Learning (FL) enables artificial intelligence frameworks to train on private information without compromising privacy, which is especially useful in the medical and healthcare industries where the knowledge or data at hand is never enough. ...

Enhancing remote patient monitoring with AI-driven IoMT and cloud computing technologies.

Scientific reports
The rapid advancement of the Internet of Medical Things (IoMT) has revolutionized remote healthcare monitoring, enabling real-time disease detection and patient care. This research introduces a novel AI-driven telemedicine framework that integrates I...

Dual smart sensor data-based deep learning network for premature infant hypoglycemia detection.

Scientific reports
In general, deficient birth weight neonates suffer from hypoglycemia, and this can be quite disadvantageous. Like oxygen, glucose is a building block of life and constitutes the significant share of energy produced by the fetus and the neonate during...

Channel attention pyramid network for remote physiological measurement.

Scientific reports
Remote photoplethysmography (rPPG) is an emerging contactless physiological parameter detection method utilizing cameras, showing great promise as a forefront technology for remote health assessment. While traditional rPPG methods have substantially ...

Advancing patient monitoring, diagnostics, and treatment strategies for transplant precision medicine.

Lancet (London, England)
Transplant medicine faces substantial challenges, as patients require lifelong immunosuppression to prevent graft rejection. Immunosuppressive regimens to date, while reasonably effective at preventing acute rejection, cause numerous health complicat...

Development of a triangular Fermatean fuzzy EDAS model for remote patient monitoring applications.

Scientific reports
Remote Patient Monitoring Systems (RPMS) are vital for tracking patients' health outside clinical settings, such as at home or in long-term care facilities. Wearable sensors play a crucial role in these systems by continuously collecting and transmit...

Fatigue monitoring using wearables and AI: Trends, challenges, and future opportunities.

Computers in biology and medicine
Monitoring fatigue is essential for improving safety, particularly for people who work long shifts or in high-demand and high-risk environments such as transportation, construction, healthcare, and manufacturing. The development of wearable technolog...

Advancing AI-driven surveillance systems in hospital: A fine-grained instance segmentation dataset for accurate in-bed patient monitoring.

Computers in biology and medicine
In the era of digital health, artificial intelligence (AI)-driven patient monitoring systems have attracted growing interest for their potential to prevent accidents in clinical settings. However, the advancement of these systems requires the availab...

Recent Advancements in Wearable Hydration-Monitoring Technologies: Scoping Review of Sensors, Trends, and Future Directions.

JMIR mHealth and uHealth
BACKGROUND: Monitoring hydration is crucial for maintaining health and preventing dehydration. Despite the potential of wearable devices for continuous hydration monitoring, health research hasn't fully explored this application, and clear design gui...