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Noninvasive Hypoglycemia Detection during Real Car Driving Using In-Vehicle Data
- Conference Paper
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. Show more
Journal / seriesDiabetes
Pages / Article No.
PublisherAmerican Diabetes Association
Organisational unit03681 - Fleisch, Elgar / Fleisch, Elgar
183569 - Design and Evaluation of a Vehicle Hypoaglycemia Warning System in Diabetes (HEADWIND Project) (SNF)
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