Background Diabetes mellitus is a metabolic disorder that affects hundreds of millions of
people worldwide and causes several million deaths every year. Such a dramatic scenario …

Using sensors to monitor signals produced by drivers is a way to help better understand how
emotions contribute to unsafe driving habits. The need for intuitive machines that can …

Aim To develop and evaluate the concept of a non‐invasive machine learning (ML)
approach for detecting hypoglycaemia based exclusively on combined driving (CAN) and …

Diabetes is a chronic disease that is escalating day by day and requires 24/7 continuous
management. It may cause many complications, precisely when a patient moves, which may …

It has been widely agreed that it is risky for patients with diabetes to drive during
hypoglycemia. However, driving during non-hypoglycemia may also bring certain safety …

Background: Hypoglycemia is a frequent and acute complication in type 1 diabetes mellitus
(T1DM) and is associated with a higher risk of car mishaps. Currently, hypoglycemia can be …

Objective: This study addresses the need to measure and monitor objective, real-world
driver safety behavior in at-risk drivers with age-related dysfunction. Older drivers are at risk …

Background Hypoglycemia, one of the most dangerous acute complications of diabetes,
poses a substantial risk for vehicle accidents. To date, both reliable detection and warning of …

Driver behavior analysis plays an important role in driver assistance systems. A driver's face
and head pose hold the key towards understanding whether the driver's attention and …

Background Kinematic driving data studies are a novel methodology relevant to health care,
but prior studies have considerable variance in their methods, populations, and findings …