RESUMEN
Cardiovascular diseases are the leading cause of mortality, morbidity, and hospitalization around the world. Recent technological advances have facilitated analyzing, visualizing, and monitoring cardiovascular diseases using emerging computational fluid dynamics, blood flow imaging, and wearable sensing technologies. Yet, computational cost, limited spatiotemporal resolution, and obstacles for thorough data analysis have hindered the utility of such techniques to curb cardiovascular diseases. We herein discuss how leveraging machine learning techniques, and in particular deep learning methods, could overcome these limitations and offer promise for translation. We discuss the remarkable capacity of recently developed machine learning techniques to accelerate flow modeling, enhance the resolution while reduce the noise and scanning time of current blood flow imaging techniques, and accurate detection of cardiovascular diseases using a plethora of data collected by wearable sensors.
RESUMEN
Wearable sensors have evolved from body-worn fitness tracking devices to multifunctional, highly integrated, compact, and versatile sensors, which can be mounted onto the desired locations of our clothes or body to continuously monitor our body signals, and better interact and communicate with our surrounding environment or equipment. Here, we discuss the latest advances in textile-based and skin-like wearable sensors with a focus on three areas, including (i) personalised health monitoring to facilitate recording physiological signals, body motions, and analysis of body fluids, (ii) smart gloves and prosthetics to realise the sensation of touch and pain, and (iii) assistive technologies to enable disabled people to operate the surrounding motorised equipment using their active organs. We also discuss areas for future research in this emerging field.