In-Vehicle Sensors to Analyze and detect Cognitive Impairment in Older Drivers
In-vehicle sensing technology has gained tremendous attention due to its ability to support major technological developments, such as connected vehicles and self-driving cars. In-vehicle sensing data are invaluable and important data sources for traffic management systems. In this paper we propose an innovative architecture of unobtrusive in-vehicle sensors and present methods and tools that are used to measure the behavior of drivers. The proposed architecture including methods and tools are used in our NIH project to monitor and identify older drivers with early dementia
A Novel Approach to Detecting and Tracking Patients in a Hospital Environment
In this paper, we present a novel approach to detect and track patients in a hospital environment. The proposed method uses a combination of computer vision techniques and machine learning algorithms to accurately identify and monitor patients in real-time. The system is designed to work with standard video cameras, making it easy to implement in existing hospital infrastructure. We evaluate the performance of our approach using a dataset of hospital images and demonstrate its effectiveness in detecting and tracking patients in various scenarios.
Body weight estimation using 3D Cameras in emergency rooms for drug dosage delivery
Weight estimation is required in adult patients when weight‐based medication must be administered during emergency care, as measuring weight is often not possible. Inaccurate estimations may lead to inaccurate drug dosing, which may cause patient harm. High‐tech 3D camera systems driven by artificial intelligence might be the solution to this problem. The aim of this review was to describe and evaluate the published literature on 3D camera weight estimation methods.