Classification of Physical Activity: Information to Artificial Pancreas Control Systems in Real Time

Abstract

Physical activity has a wide range of effects on glucose concentrations in type 1 diabetes (T1D) depending on the type (ie, aerobic, anaerobic, mixed) and duration of activity performed. This variability in glucose responses to physical activity makes the development of artificial pancreas (AP) systems challenging. Automatic detection of exercise type and intensity, and its classification as aerobic or anaerobic would provide valuable information to AP control algorithms. This can be achieved by using a multivariable AP approach where biometric variables are measured and reported to the AP at high frequency. We developed a classification system that identifies, in real time, the exercise intensity and its reliance on aerobic or anaerobic metabolism and tested this approach using clinical data collected from 5 persons with T1D and 3 individuals without T1D in a controlled laboratory setting using a variety of common types of physical activity. The classifier had an average sensitivity of 98.7% for physiological data collected over a range of exercise modalities and intensities in these subjects. The classifier will be added as a new module to the integrated multivariable adaptive AP system to enable the detection of aerobic and anaerobic exercise for enhancing the accuracy of insulin infusion strategies during and after exercise.

Keywords: aerobic and anaerobic exercise; multivariable artificial pancreas; real-time exercise classification.

Conflict of interest statement

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

© 2015 Diabetes Technology Society.

Figures

Figure 1.

The effects of different types of exercise.

Figure 2.

Bioharness-3 (Zephyr Technology, Annapolis MD).

Figure 3.

Identification of exercise type in real time.

Figure 4.

Identification of exercise type in real time for subject 1.