Activity detection and classification from wristband accelerometer data collected on people with type 1 diabetes in free-living conditions. (August 2021)

• Record Type: Journal Article
• Authors: Cescon, Marzia; Choudhary, Divya; Pinsker, Jordan E.; Dadlani, Vikash; Church, Mei Mei; Kudva, Yogish C.; Doyle III, Francis J.; Dassau, Eyal
• Is Part Of: Computers in biology and medicine. Volume 135(2021)
• Publication Date: 2021-08
• Subjects: Type 1 diabetes mellitus -- Physical activity -- Artificial pancreas -- Automated insulin delivery -- Wearable devices -- Wrist-worn accelerometer -- Supervised learning -- Free-living conditions; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104633
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: This paper introduces methods to estimate aspects of physical activity and sedentary behavior from three-axis accelerometer data collected with a wrist-worn device at a sampling rate of 32 [Hz] on adults with type 1 diabetes (T1D) in free-living conditions. In particular, we present two methods able to detect and grade activity based on its intensity and individual fitness as sedentary, mild, moderate or vigorous, and a method that performs activity classification in a supervised learning framework to predict specific user behaviors. Population results for activity level grading show multi-class average accuracy of 99.99 %, precision of 98.0 ± 2.2 %, recall of 97.9 ± 3.5 % and F1 score of 0.9 ± 0.0. As for the specific behavior prediction, our best performing classifier, gave population multi-class average accuracy of 92.43 ± 10.32 %, precision of 92.94 ± 9.80 %, recall of 92.20 ± 10.16 % and F1 score of 92.56 ± 9.94 % . Our investigation showed that physical activity and sedentary behavior can be detected, graded and classified with good accuracy and precision from three-axial accelerometer data collected in free-living conditions on people with T1D. This is particularly significant in the context of automated glucose control systems for diabetes, in that the methods we propose have the potential to inform changes in treatment parameters in response to the intensity of physical activity, allowing patients to meet their glycemic targets. Highlights: Behavior estimation from wearable devices enables precision medicine in the treatment of type 1 diabetes. Activity intensity is classified from the averaged mathematical features of 3D acceleration. Pattern recognition methods classify human behavior from 3D acceleration.