RPI presents a novel, DCL-Net system, and method based on a machine learning/DL technique to reconstruct 3D volumes from a series of 2D images. The developed technology does not require the use of sensor tracking hardware/devices (e.g., robotic arms, touchless position/pose trackers) to operate. The technology acquires multiple (two or more) consecutive, 2D US-generated image frames using a handheld imaging sensor or a sensor mounted on a motion restricted device used as inputs for estimating a US probe trajectory and tracking its motion.
Researchers at Rensselaer Polytechnic Institute are developing a non-invasive and user-friendly wearable
device for monitoring blood pressure, blood glucose, and biomarkers, which could improve quality of life,
decrease healthcare expenditure, and allow for early intervention for potentially serious diseases.
Currently, a major area of interest within the medical wearable device industry is the real-time monitoring
of blood pressure. More than 100 million adults in the United States and a third of the worldwide population