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Design and Evaluation of a Vehicle Hypoaglycemia Warning System in Diabetes (HEADWIND Project)

Applicant Stettler Christoph
Number 183569
Funding scheme Sinergia
Research institution Universitätsklinik für Diabetologie, Endokrinologie, Ernährungsmedizin & Metabolismus (UDEM)
Institution of higher education University of Berne - BE
Main discipline Interdisciplinary
Start/End 01.02.2019 - 31.01.2023
Approved amount 1'621'830.00
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All Disciplines (3)

Discipline
Interdisciplinary
Information Technology
Clinical Endocrinology

Keywords (9)

decision support system; driving; digital markers; automotive technology; driver feedback; diabetes mellitus; traffic safety; predictive analytics; hypoglycemia

Lay Summary (German)

Lead
Unterzuckerungen (Hypoglykämien) können eine schwerwiegende akute Komplikation eines mit Insulin oder gewissen anderen Medikamenten therapierten Diabetes mellitus darstellen. Diese äussern sich durch eine Verminderung der Konzentration, einer Verlangsamung von Auffassung und Denkprozessen sowie Einschränkungen zahlreicher psychomotorischer Funktionen. Dies ist insbesondere im Strassenverkehr kritisch, wo rasche Entscheidungsabfolgen unter Integration zahlreicher Faktoren unabdingbar sind.
Lay summary

Inhalt und Ziel des Forschungsprojektes

Das übergeordnete Ziel des Projektes HEADWIND («Design and Evaluation of a Vehicle Hypoglycemia Warning System in Diabetes») besteht in einem neuartigen Ansatz zur Verbesserung der Verkehrssicherheit für Patientinnen und Patienten mit einem Diabetes mellitus. Um das aufgrund der Hypoglykämiegefahr erhöhte Unfallrisiko von Personen mit Diabetes mellitus zu reduzieren werden die immensen Möglichkeiten der sich rasant entwickelnden Automobilindustrie mit innovativen Ansätzen aus dem Bereich der künstlichen Intelligenz gepaart. Das Forscherteam beabsichtigt, Hypoglykämien direkt aus den vom Fahrzeug während der Autofahrt in Echtzeit gewonnenen Daten zu detektieren. Bereits heute werden während der Autofahrt hunderte von Fahrparametern erfasst. Diese Daten sollen nun genutzt und mittels sogenanntem «maschinellem Lernen» laufend analysiert werden, um Veränderungen des Fahrverhaltens zu erkennen, welche auf eine Hypoglykämie hindeuten. In einem ersten Schritt werden Untersuchungen am Fahrsimulator durchführt, wobei Patienten mit Diabetes unter ärztlicher Überwachung in eine Hypoglykämie versetzt werden. In einem nächsten Schritt werden diese Untersuchungen auf abgesperrten Teststrecken in echte Autos auf die Strasse verlagert. Eine grosse Herausforderung dieses Projektes besteht nebst der Datenextraktion- und Echtzeitverarbeitung unter Anwendung komplexer mathematischer Algorithmen («machine-learning») insbesondere in der kontrollierten Herbeiführung einer Hypoglykämie im fahrenden Auto.

Wissenschaftlicher und gesellschaftlicher Kontext des Forschungsprojektes

Diabetes mellitus ist eine rasch zunehmende Erkrankung mit weltweit knapp 500 Millionen und in der Schweiz ca. 500’000 betroffenen Patienten/innen. Ein beträchtlicher Anteil davon ist im Rahmen ihrer Therapie einem erhöhten Risiko von Hypoglykämien ausgesetzt und fährt regelmässig Auto. Um die Gefahr von Unfällen im Strassenverkehr zu reduzieren bedarf es neuer, innovativer Ansätze, welche die sich bietenden Möglichkeiten aus Automobilindustrie und künstlicher Intelligenz vereinen.

Direct link to Lay Summary Last update: 03.12.2018

Responsible applicant and co-applicants

Employees

Project partner

Publications

Publication
Towards Wearable-based Hypoglycemia Detection and Warning in Diabetes
Maritsch Martin, Föll Simon, Lehmann Vera, Bérubé Caterina, Kraus Mathias, Feuerriegel Stefan, Kowatsch Tobias, Züger Thomas, Stettler Christoph, Fleisch Elgar, Wortmann Felix (2020), Towards Wearable-based Hypoglycemia Detection and Warning in Diabetes, in CHI '20: CHI Conference on Human Factors in Computing Systems, Honolulu HI USAACM Digital Library, USA.

Collaboration

Group / person Country
Types of collaboration
Automobil Club der Schweiz (ACS) Switzerland (Europe)
- Research Infrastructure
The Federal Department of Defence, Civil Protection and Sport (DDPS) Switzerland (Europe)
- Research Infrastructure

Scientific events

Active participation

Title Type of contribution Title of article or contribution Date Place Persons involved
Annual Assembly Schweizerische Gesellschaft für Endokrinologie und Diabetes (SGED) 2020, Bern, Switzerland Poster Individual Estimation of Blood Glucose and Self-Assessment of Driving Performance in Individuals with Type 1 Diabetes Driving in Hypoglycemia 12.11.2020 Bern, Switzerland Züger Thomas; Stettler Christoph; Wortmann Felix; Lehmann Vera; Laimer Markus; Maritsch Martin; Feuerriegel Stefan; Lagger Sophie; Kowatsch Tobias; Bérubé Caterina; Albrecht Caroline; Fleisch Elgar; Kraus Mathias; Styger Naïma;
Annual Assembly Schweizerische Gesellschaft für Endokrinologie und Diabetes (SGED) 2020, Bern, Switzerland Poster HEADWIND: Design and Evaluation of a Vehicle Hypoglycemia Warning System in Diabetes – Results from a Driving Simulator Study 12.11.2020 Bern, Switzerland Lagger Sophie; Kraus Mathias; Wortmann Felix; Züger Thomas; Feuerriegel Stefan; Laimer Markus; Albrecht Caroline; Lehmann Vera; Fleisch Elgar; Kowatsch Tobias; Styger Naïma; Bérubé Caterina; Maritsch Martin; Stettler Christoph;
Virtual Day of Biomedical Research, University of Bern 2021 Talk given at a conference Headwind: design and evaluation of a vehicle hypoglycaemia warning system in diabetes:– Results from a driving simulator study 05.11.2020 Bern, Switzerland Lagger Sophie; Albrecht Caroline; Maritsch Martin; Kowatsch Tobias; Stettler Christoph; Wortmann Felix; Styger Naïma; Laimer Markus; Bérubé Caterina; Fleisch Elgar; Feuerriegel Stefan; Kraus Mathias; Züger Thomas; Lehmann Vera;
EASD Virtual Meeting 2020 Poster Headwind: design and evaluation of a vehicle hypoglycaemia warning system in diabetes: a proof of principle study 21.10.2020 Vienna (Virtual Meeting), Austria Maritsch Martin; Züger Thomas; Bérubé Caterina; Lehmann Vera; Laimer Markus; Fleisch Elgar; Feuerriegel Stefan; Kraus Mathias; Stettler Christoph; Wortmann Felix; Kowatsch Tobias;
Annual Assembly Schweizerische Gesellschaft für Endokrinologie und Diabetes (SGED) 2019, Bern, Switzerland Poster HEADWIND: Design and Evaluation of a Vehicle Hypoglycemia Warning System in Diabetes - a proof of principle study 14.11.2019 Bern, Switzerland Kraus Mathias; Lehmann Vera; Stettler Christoph; Fleisch Elgar; Feuerriegel Stefan; Maritsch Martin; Kowatsch Tobias; Laimer Markus; Wortmann Felix;


Knowledge transfer events

Active participation

Title Type of contribution Date Place Persons involved
GHF Expert Meeting: What are the current challenges and solutions to access to insulin and diabetes care? Talk 11.11.2019 Geneva, Switzerland Züger Thomas;


Self-organised

Title Date Place
HEADWIND: project presentation to the DDPS and ACS 16.07.2020 Thun, Switzerland

Awards

Title Year
Research Prize Alumni MedBern 2020 2020

Abstract

Main goals: Hypoglycemia is among the most relevant acute complications of diabetes mellitus. It affects neuro-cognitive and psychomotor function and has consistently been shown to be associated with an increased risk of driving mishaps and accidents. To prevent these, our project aims at designing, implementing and evaluating a vehicle hypoglycemia warning system “HEADWIND”. The overall goal is to detect hypoglycemia in an early stage with a sensing module and then trigger direct interventions through a support module. For this purpose, we draw upon current automotive sensor technology and innovative digital markers of vegetative function. Novelty: Hypoglycemia has been widely studied in medical research, yet the focus has been limited to the ex post understanding of how behavior and especially driving patterns are afflicted. This differs substantially from the current project, as our key innovation is to apply machine learning to neuro-cognitive and psychomotor function, affected automotive parameters, as well as physiological markers in order to reliably detect hypoglycemia and give early and effective warnings. First, we strive for advanced analytics that can generalize to unseen patients in order to serve as an early warning system. Second, we actively search for digital markers that entail diagnostic capacity of hypoglycemia and have not been subject of previous studies. Examples include high-resolution time series with real-time driving data and physiological sensors, but also video streams of drivers’ facial expressions. Results will be compared against state-of-the-art continuous glucose sensors (CGM). Third, we ensure readily application of our findings by providing a functional, open source prototype of our system.Broader impact: Diabetes mellitus is a chronic disease with rapidly increasing prevalence. A considerable number of these patients are under treatments associated with an increased risk for hypoglycemia. Despite important developments in the field of diabetes technology (e.g. CGM), the problem of hypoglycemia during driving persists due to the limited availability of such technology and technical/behavioral limitations of available methods (e.g. non-continuous character of traditional self-measurement of blood glucose [SMBG], and delayed response to changes in glucose levels/non-adherence to warnings by CGM). Hence, alternative approaches to solve this issue are urgently needed. Our work promises to reduce driving accidents induced by hypoglycemia in the near future since the proposed approach natively integrates with current automotive technology. Furthermore, the availability of a reliable detection model in diabetic individuals will also shed light on similar problems in non-diabetic patients at risk for hypoglycemia, especially the rapidly growing population of post-bariatric patients (i.e. patients after surgery for obesity). Finally, the establishment of a reliable algorithm detecting medical conditions based on individual behavior may also be transferrable to other fields, especially in situations where humans control critical processes or infrastructure (e.g. when operating hazardous machines or handling demanding tasks). Interdisciplinary research: This project combines medical research (clinical endocrinology) on diabetes mellitus and information technology (applied computer science, health information systems, ubiquitous computing). This is reflected by our work packages. Specifically, we first determine potential digital markers and evidence-based warnings in a clinical lab study with a driving simulator (WP1). We then cross-validate the findings and enhance the markers and warnings separately in a controlled field study on a test track and a driving school car with instructor (WP2). Finally, markers and warnings are integrated and the clinical relevance of HEADWIND is evaluated in the field (WP3), i.e. by studying response rates of subjects to the warnings (such as safely stopping the vehicle). Implementation: The resulting open source HEADWIND prototype can directly be integrated into state-of-the-art sensor technology and on-board systems of current cars (e.g. as used for detecting driver drowsiness). This is especially relevant as autonomous cars of level 4/5 are still forecasted to be several decades away.
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