A project to develop and evaluate a hypoglycaemia warning system during car journeys
The interdisciplinary HEADWIND project pursues an innovative approach to improve road safety for people with type 1 diabetes. Today, vehicle data are collected in modern cars in real time while driving in order to avoid accidents. Hypoglycaemia can be a serious acute complication of type 1 diabetes. Hypoglycaemia manifests by reductions in concentration, alertness and restrictions in, for example, body movement. This is especially critical in road traffic, where complex decisions have to be made quickly. Despite important developments in diabetes technology, the problem of hypoglycaemia still exists when driving. Therefore, alternative approaches to improve driving safety in patients with diabetes are urgently needed. The aim of this study is to develop problem-solving strategies capable to distinguish driving patterns in normal blood glucose and in hypoglycaemia using machine learning. In a first step, investigations will be carried out on a driving simulator, with patients being put into hypoglycaemia under medical supervision. In a next step, these examinations will be transferred to a real car on closed-off test tracks.
- Swiss National Science Foundation (SNSF)
- Clinical Trials Unit (CTU) Bern
- Diabetes Center Berne (DCB)
- Swiss Diabetes Society (SDS)
- ETH Zurich: Prof. Dr. Elgar Fleisch, Prof. Dr. Tobias Kowatsch, Prof. Dr. Stefan Feuerriegel, Prof. Dr. Mathias Kraus, Martin Maritsch, Caterina Bérubé
- University of St. Gallen: Prof. Dr. Elgar Fleisch, Prof. Felix Wortmann