Filtering and Prediction of the Blood Glucose Concentration using an Android Smart Phone and a Continuous Glucose Monitor

TL;DR

This paper evaluates the performance of Java linear algebra libraries and implements Kalman filters on an Android smartphone for real-time blood glucose monitoring using continuous glucose monitor data.

Contribution

It demonstrates the implementation and comparison of Kalman filters on a smartphone for glucose estimation, highlighting EJML's superior performance among libraries.

Findings

EJML outperforms JAMA and Apache Common Math in linear algebra tasks.

Kalman filters effectively estimate glucose levels from CGM data on a smartphone.

Real-time filtering is feasible on mobile devices for diabetes management.

Abstract

In this paper we numerically assess the performance of Java linear algebra libraries for the implementation of nonlinear filters in an Android smart phone (Samsung A5 2017). We implemented a linear Kalman filter (KF), an extended Kalman filter (EKF), and an unscented Kalman filter (UKF). These filters are used for state and parameter estimation, as well as fault detection and meal detection in an artificial pancreas. We present the state estimation technologies used for glucose estimation based on a continuous glucose monitor (CGM). We compared three linear algebra libraries: The Efficient Java Matrix Library (EJML), JAMA and Apache Common Math. Overall, EJML provides the best performance for linear algebra operations. We demonstrate the implementation and performance of filtering (KF, EKF and UKF) using real CGM data.