Introduction to the Special Issue on Internet-of-Medical-Things

Abstract

The Internet-of-Medical Things (IoMT) paradigm paves the foundations for an intelligent and reliable per-sonalized precision medicine. This embedded computing paradigm aims to offer accurate multiscale medical monitoring through smart sensing, advanced analytics, and enabling continuous and rigorous medical diagno-sis, and on-the-fly communication with medical experts. It leverages mathematical and physical modeling of human anatomy and physiology to provide hyperspectral and hyperdimensional processing and restores health through precise patient-specific actuation. Along these lines of providing advanced analytics for early detection and injury of falls, in “Pervasive Pose Estimation for Fall Detection”, Luo et al. proposed a pervasive pose estimation strategy for fall detection (P2Est) on a mobile portable device (e.g., smartphone) capable to quantify changes in tilt angle and height of the human body. To quantify the tilt measurement, the P2Est exploits the pointing of the mobile device to associate the device coordinate system with the world coordinate system. To gauge the height changes, the P2Est considers that the person’s height remains relatively unchanged while walking to calibrate the pressure difference between the device and the floor. Luo et al. implemented the P2Est strategy and tested it in various environments demonstrating that it can track the body orientation irrespective of which pocket the phone is placed in. The authors also report that P2Est strategy exploits the phone’s barometer to detect falls in various environments with decimeter-level accuracy.