Abstract
Background and Aims
Maintaining glucose concentration (GC) in the target range in spite of physical activities and events causing acute psychological stress (PS) is challenging for artificial pancreas (AP) systems. PS and physical activities affect GC in different ways; aerobic exercise decreases GC while PS can increase it. Many stressful events and physical activities cannot be manually entered to the AP in a timely manner. Hence, they are unknown disturbances for AP systems and reduce glycemic control. In this work, wristband biosignals are utilized in a novel algorithm to estimate the psychological and physiological state of a subject, and improve the GC prediction for use in AP systems.
Methods
Biosignals from Empatica E4 wristband are collected in real-time and machine learning algorithms are utilized to determine the physical state of a subject, obtain energy expenditure estimates, and predict her/his PS levels. These estimates are incorporated in a GC prediction model along with CGM readings and insulin infusion data from pump. These BGC estimates are compared to estimates from a model that uses only CGM readings and insulin infusion data.
Results
Data from 50 experiments with thirteen different subjects with T1D who performed physical activities and various PS causing events were used and proposed method improved to mean absolute percentage error of GC prediction by 6.5%.
Conclusions
Wristband biosignals used to determine the psychological and physiological state of people with T1D provide valuable information to improve GC estimates and the performance of an AP system in response to unannounced physical activities and stressful events.
