A deep learning-assisted skin-integrated pulse sensing system for reliable pulse monitoring and cardiac function assessment

Advancements in Bio-Integrated Flexible Electronics for Hemodynamic Monitoring in Cardiovascular Healthcare

Cardiovascular diseases (CVDs) remain the leading cause of global mortality, highlighting the urgent need for effective monitoring and prevention strategies. The rapid advancement of flexible sensing technology and the development of conformal sensors have attracted significant attention due to their potential for continuous, real-time assessment of cardiovascular health over extended periods. This review outlines recent advancements in bio-integrated flexible electronics designed for hemodynamic monitoring and broader CVD healthcare applications. It introduces key physiological indicators relevant to hemodynamics, including heart rate, blood pressure, blood flow velocity, and cardiac output. Next, it discusses flexible bio-integrated electronics engineering strategies, such as working principles and configuration designs. Various non-invasive and invasive bio-integrated devices for monitoring these hemodynamic indicators are then presented. Additionally, the review highlights the role of artificial intelligence algorithms and their practical applications in bio-integrated electronics for hemodynamic detection. Finally, it proposes future directions and addresses potential challenges in the field.

Advances in Machine Learning for Wearable Sensors

Recent years have witnessed tremendous advances in machine learning techniques for wearable sensors and bioelectronics, which play an essential role in real-time sensing data analysis to provide clinical-grade information for personalized healthcare. To this end, supervised learning and unsupervised learning algorithms have emerged as powerful tools, allowing for the detection of complex patterns and relationships in large, high-dimensional data sets. In this Review, we aim to delineate the latest advancements in machine learning for wearable sensors, focusing on key developments in algorithmic techniques, applications, and the challenges intrinsic to this evolving landscape. Additionally, we highlight the potential of machine-learning approaches to enhance the accuracy, reliability, and interpretability of wearable sensor data and discuss the opportunities and limitations of this emerging field. Ultimately, our work aims to provide a roadmap for future research endeavors in this exciting and rapidly evolving area.