Smartwatches for Non-Invasive Hypoglycaemia Detection During Cognitive and Psychomotor Stress

MCML Authors

Stefan Feuerriegel

Prof. Dr.

Principal Investigator

Artificial Intelligence in Management

Abstract

Hypoglycaemia is one of the most relevant complications of diabetes1 and induces alterations in physiological parameters2, 3 that can be measured with smartwatches and detected using machine learning (ML).4 The performance of these algorithms when applied to different hypoglycaemic ranges or in situations involving cognitive and psychomotor stress remains unclear. Demanding tasks can significantly affect the physiological responses on which the wearable-based hypoglycaemia detection relies.5 The present analysis aimed to investigate ML-based hypoglycaemia detection using wearable data at different levels of hypoglycaemia during a complex task involving cognitive and psychomotor challenges (driving).

article

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Diabetes, Obesity and Metabolism

26.3. Mar. 2024.

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Authors

M. Maritsch • S. Föll • V. Lehmann • N. Styger • C. Bérubé • M. Kraus • S. Feuerriegel • T. Kowatsch • T. Züger • E. Fleisch • F. Wortmann • C. Stettler

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Links

DOI

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Research Area

 A1 | Statistical Foundations & Explainability