A Machine Learning–Based Approach to Noninvasively Detect Hypoglycemia from Gaze Behavior While Driving
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Date
2021-06-21Type
- Other Conference Item
ETH Bibliography
yes
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Abstract
Aim: To non-invasively detect hypoglycemia in individuals with type 1 diabetes (T1D) based on gaze behavior while driving.
Methods: Controlled hypoglycemia was induced in 19 individuals (12 males, age 32 ± 7.1 yrs) with T1D (HbA1c 7.1 ± 0.6% [54 ± 6 mmol/mol]) using an adapted hypoglycemic clamp protocol. Gaze and blood glucose (BG) data were gathered while driving in a simulator during three 18 min sessions: session 1 (BG 90-144 mg/dL), session 2 (BG declining from 72 to 45 mg/dL), and session 3 (BG 36-45 mg/dL). A gradient-boosting machine learning (ML) model was built for hypoglycemia (BG < 70 mg/dL) detection based on gaze behavior.
Results: Mean venous BG was 105.4 ± 11.4 mg/dL during session 1, declined from 61.4 ± 6.1 mg/dL to 47.2 ± 8.5 mg/dL during session 2, and was 42.7 ± 4.1 mg/dL during session 3, respectively. Gaze analysis provided 29,968 data samples (1,577.5 ± 52 per subject, 10,041 euglycemia, 19,927 hypoglycemia). Overall, ML achieved an area under the receiver-operating-characteristics curve of 0.83 ± 0.09 for hypoglycemia detection with leave-one-subject-out cross-validation.
Conclusion: ML-based gaze analysis shows high accuracy in non-invasive hypoglycemia detection while driving. Our approach offers promising potential in various settings where cameras are available. Show more
Publication status
publishedExternal links
Journal / series
DiabetesVolume
Pages / Article No.
Publisher
American Diabetes AssociationEvent
Organisational unit
03681 - Fleisch, Elgar / Fleisch, Elgar
09623 - Feuerriegel, Stefan (ehemalig) / Feuerriegel, Stefan (former)
Funding
183569 - Design and Evaluation of a Vehicle Hypoaglycemia Warning System in Diabetes (HEADWIND Project) (SNF)
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ETH Bibliography
yes
Altmetrics