Details

Autor(en) / Beteiligte

• LEHMANN, VERA
• ZUEGER, THOMAS
• MARITSCH, MARTIN
• NOTTER, MICHAEL
• SCHALLMOSER, SIMON
• BÉRUBÉ, CATERINA
• ALBRECHT, CAROLINE
• KRAUS, MATHIAS
• FEUERRIEGEL, STEFAN
• FLEISCH, ELGAR
• KOWATSCH, TOBIAS
• LAGGER, SOPHIE N.
• LAIMER, MARKUS
• WORTMANN, FELIX
• STETTLER, CHRISTOPH

Titel

234-OR: Noninvasive Hypoglycemia Detection during Real Car Driving Using In-Vehicle Data

Ist Teil von

• Diabetes (New York, N.Y.), 2022-06, Vol.71 (Supplement_1)

Ort / Verlag

New York: American Diabetes Association

Erscheinungsjahr

2022

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Aim: To develop a non-invasive machine learning (ML) approach to detect hypoglycemia during real car driving based on driving (CAN) , and eye and head motion (EHM) data. Methods: We logged CAN and EHM data in 21 subjects with type 1 diabetes (18 male, 41 ± yrs, A1c 6.8 ± 0.7 % [51 ± 7 mmol/mol]) during driving in eu- (EU) and hypoglycemia (< 3.0 mmol/L, HYPO) . Participants drove in a car (Volkswagen Touran) supervised by a driving instructor on a closed test-track. Using CAN and EHM data, we built ML models to predict the probability of the driver being in HYPO. To make our approach applicable to different generations of cars, we present 3 ML models: first, a model combining CAN+EHM, representing the modern car with integrated camera. Second, a CAN model using driving data only, since modern cars are not generally equipped with EHM tracking. Third, anticipating that autonomous driving will limit the role of CAN data in the future, we tested a model solely based on EHM. Results: Mean BG in EU and HYPO was 6.3 ± 0.8 mmol/L and 2.5 ± 0.5 mmol/L (p< 0.001) , respectively. The model CAN+EHM achieved an area under the receiver operating characteristic curve of 0.88 ± 0.05, sensitivity of 0.70 ± 0.30, and specificity of 0.83 ± 0.in detecting HYPO. Further results are in Fig. 1. Conclusion: We propose ML-based approaches to non-invasively detect HYPO from driver behavior, applicable to contemporary cars and anticipating developments in automotive technology. Disclosure V.Lehmann: None. E.Fleisch: None. T.Kowatsch: Advisory Panel; Pathmate Technologies AG, Research Support; CSS Insurance, Stock/Shareholder; Pathmate Technologies AG. S.N.Lagger: None. M.Laimer: None. F.Wortmann: None. C.Stettler: None. T.Zueger: None. M.Maritsch: None. M.Notter: None. S.Schallmoser: None. C.Bérubé: None. C.Albrecht: None. M.Kraus: None. S.Feuerriegel: None. Funding Swiss National Science Foundation (SNF CRSII5_183569) , Swiss Diabetes Foundation, Diabetes Center Berne, Automobile Club Switzerland (ACS) , Federal Department of Defence, Civil Protection and Sport (DDPS) and Department of Research of the University Hospital Berne