Machine learning for non‐invasive sensing of hypoglycaemia while driving in people with diabetes

Aim: To develop and evaluate the concept of a non-invasive machine learning (ML) approach for detecting hypoglycaemia based exclusively on combined driving (CAN) and eye tracking (ET) data. Materials and Methods: We first developed and tested our ML approach in pronounced hypoglycaemia, and then we applied it to mild hypoglycaemia to evaluate its early warning potential. For this, we conducted two consecutive, interventional studies in individuals with type 1 diabetes. In study 1 (n = 18), we collected CAN and ET data in a driving simulator during euglycaemia and pronounced ypoglycaemia (blood glucose [BG] 2.0-2.5 mmol L-1). In study 2 (n = 9), we collected CAN and ET data in the same simulator but in euglycaemia and mild hypoglycaemia (BG 3.0-3.5 mmol L-1). Results: Here, we show that our ML approach detects pronounced and mild hypoglycaemia with high accuracy (area under the receiver operating characteristics curve 0.88 ± 0.10 and 0.83 ± 0.11, respectively). Conclusions: Our findings suggest that an ML approach based on CAN and ET data, exclusively, enables detection of hypoglycaemia while driving. This provides a promising concept for alternative and non-invasive detection of hypoglycaemia

Smartwatches for non-invasive hypoglycaemia detection during cognitive and psychomotor stress.

ISSN:1462-8902ISSN:1463-132

Machine learning for non-invasive sensing of hypoglycemia while driving in people with diabetes.

AIMS Hypoglycemia is one of the most dangerous acute complications of diabetes mellitus and is associated with an increased risk of driving mishaps. Current approaches to detect hypoglycemia are limited by invasiveness, availability, costs, and technical restrictions. In this work, we developed and evaluated the concept of a non-invasive machine learning (ML) approach detecting hypoglycemia based exclusively on combined driving (CAN) and eye tracking (ET) data. MATERIALS AND METHODS We first developed and tested our ML approach in pronounced hypoglycemia, and, then, we applied it to mild hypoglycemia to evaluate its early warning potential. For this, we conducted two consecutive, interventional studies in individuals with type 1 diabetes mellitus. In study 1 (n=18), we collected CAN and ET data in a driving simulator during eu- and pronounced hypoglycemia (blood glucose [BG] 2.0 - 2.5 mmol L-1 ). In study 2 (n=9), we collected CAN and ET data in the same simulator but in eu- and mild hypoglycemia (BG 3.0 - 3.5 mmol L-1 ). RESULTS Here, we show that our ML approach detects pronounced and mild hypoglycemia with high accuracy (area under the receiver operating characteristics curve [AUROC] 0.88±0.10 and 0.83±0.11, respectively). CONCLUSIONS Our findings suggest that an ML approach based on CAN and ET data, exclusively, allows for detection of hypoglycemia while driving. This provides a promising concept for alternative and non-invasive detection of hypoglycemia. This article is protected by copyright. All rights reserved