Meal and Physical Activity Detection from Free-living Data for Discovering Disturbance Patterns to Glucose Levels in People with Diabetes.

ABSTRACT:

Objective

Interpretation of time series data collected in free-living has gained importance in chronic disease management. Some data are collected objectively from sensors and some are estimated and entered by the individual. In type 1 diabetes (T1D), blood glucose concentration (BGC) data measured by continuous glucose monitoring (CGM) systems and insulin doses administered can be used to detect the occurrences of meals and physical activities and generate the personal daily living patterns for use in automated insulin delivery (AID).

Methods

Two challenges in time-series data collected in daily living are addressed: data quality improvement and detection of unannounced disturbances to BGC. CGM data have missing values for varying periods of time and outliers. People may ne

SUBMITTER: Askari MR 

PROVIDER: S-EPMC10038808 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature