With our current project we would like to predict the occurrence of hypo- and hyperglycemia in type 1 & type 2 diabetes patients, identify the causes and propose individual countermeasures. We would like to develop an application for physicians and patients that combines data from blood glucose sensors, nutrition diaries and wearables such as smartwatches and activity trackers. Machine learning algorithms enable us to propose individual measures for disease improvement. Through this project we want to improve the quality of life and long-term risk of diabetes patients.
Monitoring of the blood sugar level with the help of various continuous glucose monitoring systems.
Documentation of meals via various digital nutrition diaries on the smartphone.
Measurement of movement, sleep, heart rate and calorie consumption via wearables such as Activity Tracker and Smartwatches.
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Diabetes is a challenging and drastic disease that requires strict self-control. Patients therefore have to make countless decisions every day. These decisions affect factors such as nutrition, blood glucose monitoring and physical activity, and have a massive impact on lifestyle and everyday life. Options for action have to be constantly reconsidered and adapted to individual situations. The goal of our project is to predict hypo- and hyperglycaemia promptly, to recognize causes and to propose individual measures. There is no single solution since the factors vary from person to person.
Current software and applications for diabetes patients focus either on monitoring and user friendly calculation of insulin dosage or on manual data collection and information provision. There is a lack of individual and timely feedback that brings together and evaluates all of the user's data to identify foods, routines and events that affect the daily course of the disease.
Although every person and lifestyle is individual, treatment is largely standardised. Diabetes management is complex and requires the evaluation of various medical and personal data such as blood sugar levels, insulin dosage and the individual circumstances of the patients.
We would like to develop a user friendly application that collects data from sensors and the smartphone and merges it via interfaces. By combining this data individual insights into the lives of patients can be gained and tailored recommendations and therapies can be developed.
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*The view expressed in such publications can in no way be taken to reflect the official opinion of the European Space Agency