Adaptive, Intelligent, and Mobile Control of Artificial Pancreas (AIM-CAP)
The AIM-CAP group is an interdisciplinary team of researchers from School of Engineering and School of
Pharmacy, pursuing integration of engineering approaches into biological and medical sciences. The
group primarily investigates closed-loop control of artificial pancreas based on engineering methods,
including machine learning, simulation, and optimization, to help patients with diabetes mellitus.
Diabetes mellitus (DM) is a serious and widespread disease currently affecting 30 million people in the
United States. DM is caused by either failure of human body’s ability to produce insulin (Type 1) or
defects in insulin secretion by pancreatic beta-cells and insulin action in the peripheral tissues (Type 2).
If left untreated, DM can cause serious complications such as cardiovascular disease, stroke, kidney
failure, foot ulcers, and damage to eyes. Treatments including exercise, diet, hypoglycemic pills, and
insulin often are not sufficient to mitigate the symptoms of the disease or prevent its progression over
time.
An artificial pancreas (AP) capable of continuously monitoring and adjusting blood glucose levels
through closed-loop control, thus maintaining a normal range of glucose levels (70-130 mg/dl) at all
times, will be an improved treatment for the disease. One of the largest hurdles in developing an AP is
the creation of an individualized algorithm capable of calculating insulin doses from blood glucose
readings and controlling a pump to deliver the appropriate doses of insulin. Our AIM is to develop
Adaptive and Intelligent control algorithms using Mobile devices, which provide more accurate,
effective, and individualized control for each patient and ease the management of blood glucose level.
Principal Investigators
Hoo Sang Ko, Ph.D. (Industrial Engineering)
Guim Kwon, Ph.D. (Pharmaceutical Science)
H. Felix Lee, Ph.D. (Industrial Engineering)
Graduate Research Students
Sakineh Esmaeili
Saeid Bahremand
Duygu Durak
Michael Brenner
Research Topics
1. Simulation of glucose-insulin homeostasis model
As a step toward developing algorithms for an AP, this project applies simulation technology with a
mathematical model of glucose-insulin homeostasis in order to study the interaction between
glucose and insulin in healthy and diabetic mice and rats. We first gathered data experimentally
using intra-peritoneal glucose and insulin tolerance tests on mice and rats in various stages of
diabetes progression. We applied a dynamic hybrid simulation method to the mathematical model
with a set of fixed parameter values (8 rate constants) to predict glucose levels over time. Then we
incorporated an optimum search method into the simulation in order to find the best estimates for
the 8 parameters. The predicted data fit accurately to the observed data for healthy, pre-diabetic,
and diabetic mice, validating the use of the mathematical model with the optimum search method
for mice and rats at different stages of diabetes. Examination of the rate constants may provide
insights into which parameters are most affected by the progression of diabetes and thus how
tightly a control algorithm needs to be calibrated to maintain proper control.
2. Prediction and control of AP using machine learning techniques
In the past decade, control algorithms for AP have been developed based on traditional methods
such as PID control and Model Predictive Control; however, these methods were not effective
enough due to variability in metabolism between or within individuals. This project aims to develop
an adaptive algorithm using Artificial Neural Networks (ANN) to predict blood glucose level (BGL) in
DM patients. Glucose level data collected from DM rats using CGM are used along with other inputs
such as insulin injection and meal information to develop the ANN for each individual. In a
preliminary study, the ANN prediction model performed well with an error rate of less than 5 mg/dL
(2%) for 5-minute prediction and about 15 mg/dL (8%) for 30-minute prediction. A closed-loop
control algorithm based on another ANN is being developed on top of the ANN prediction model can
be used for closed-loop control of BGL of T1DM patients to maintain BGL within normal range and
thus avoid hypo-/hyper-glycemia.
3. Mobile applications for closed-loop control of AP (based on Duygu)
AP is composed of three components: continuous glucose monitor (CGM), insulin pump, and control
algorithm involving both. So far, developing and testing control algorithms were conducted in very
limited settings as the algorithms had to be implemented in desktop or laptop computers wired to a
CGM and an insulin pump. This limitation complicated real-time closed-loop control of AP. This
project aims at developing a mobile app to implement AP control algorithms for continuous
monitoring, prediction and control on a user-friendly interface with ubiquitous access. The mobile
app collects CGM data and manually entered glucose intake and exercise level. The app transmits
the data to a remote MATLAB/.NET server, where the control algorithm computes appropriate level
of insulin injection based on the data received. Since the algorithm is housed and running in the
remote server, the mobile app only needs to provide means to access the control algorithm, which
makes it easy to develop and test a variety of control algorithms. Finally, the output from the
remote server is sent to the mobile app and then to the insulin pump in AP. These steps are
repeated to realize the closed-loop control. The mobile app has been developed as an Android app
to collect data and to communicate with the server. The server runs the algorithm as well as other
management services for communication and monitoring.