Towards Wearable-based Hypoglycemia Detection and Warning in Diabetes
Open accessAbstract
Rigorous blood glucose management is vital for individuals with diabetes to prevent states of too low blood glucose (hypoglycemia). While there are continuous glucose monitors available, they are expensive and not available for many patients. Related work suggests a correlation between the blood glucose level and physiological measures, such as heart rate variability. We therefore propose a machine learning model to detect hypoglycemia on basis of data from smartwatch sensors gathered in a proof-of-concept study. In further work, we want to integrate our model in wearables and warn individuals with diabetes of possible hypoglycemia. However, presenting just the detection output alone might be confusing to a patient especially if it is a false positive result. We thus use SHAP (SHapley Additive exPlanations) values for feature attribution and a method for subsequently explaining the model decision in a comprehensible way on smartwatches.
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Features of heart rate, heart rate variability, movement, and electrodermal activity extracted from wearable data have been applied to detect hypoglycemic events in cohorts ranging from 1 to 36 individuals.
“Features of HR, HRV, movement, and electrodermal activity (EDA; a measure of perspiration) extracted from wearable data have been applied to detect hypoglycemic events in cohorts ranging from 1 to 36 individuals5,26–28.”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [26]
Prior work typically uses less than a day of input.
“First, where prior work typically uses less than a day of input5,22–32, we assess the impact of longitudinal input windows ranging up to multiple weeks and demonstrate improved sensitivity with longer windows (AUROC increases by 0.15 from 1-night to 21-night models, in balanced and imbalanced cohorts).”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [26]
Prior work chiefly assessed statistical aggregation functions of wearable data streams (primary features), which are nonspecific to the effects of DM within data streams that are constantly fluctuating in natural settings.
“Third, prior work chiefly assessed statistical aggregation functions of wearable data streams (primary features)5,26–32, which are nonspecific to the effects of DM within data streams that are constantly fluctuating in natural settings.”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [26]
Prior work was generated from small cohorts (the largest cohort of 70 individuals), raising questions of the generalizability of their findings.
“Second, prior work was generated from small cohorts (the largest cohort of 70 individuals), that raise questions of the generalizability of their findings5,22–32.”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [26]
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- Authors
- Maritsch, M., Föll, S., Lehmann, V., Bérubé, C., Kraus, M., Feuerriegel, S., Kowatsch, T., Züger, T., Stettler, C., Fleisch, E., & Wortmann, F.
- Affiliations
- ETH Zurich (CH)
- Karlsruhe Institute of Technology (DE)
- University Hospital of Bern (CH)
- University of St.Gallen (CH)
- ORCID
- Venue
- Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2020-04-25
- Pages
- 1-8
- OpenAlex
- W3006524040
- Cited by
- 35
- References
- 18
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 4 · 2023: 14 · 2022: 10 · 2021: 2
- FWCI
- 27.70
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) (publishedVersion)
- Alexandria (UniSG) (University of St.Gallen) (acceptedVersion)
- Alexandria (UniSG) (University of St.Gallen) (submittedVersion)
- BORIS (University Library Bern) (submittedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, other-oa)
- ETH Zürich Research Collection
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- hypoglycemia detection, diabetes mellitus, wearable sensors, smartwatches, heart rate variability, blood glucose monitoring, machine learning, SHAP, explainable AI, decision explanation
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