Award Abstract # 1446900
CPS: Synergy: Collaborative Research: In-Silico Functional Verification of Artificial Pancreas Control Algorithms.

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: THE REGENTS OF THE UNIVERSITY OF COLORADO
Initial Amendment Date: September 9, 2014
Latest Amendment Date: March 3, 2017
Award Number: 1446900
Award Instrument: Standard Grant
Program Manager: Anindya Banerjee
abanerje@nsf.gov
 (703)292-7885
CNS
 Division Of Computer and Network Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: October 1, 2014
End Date: September 30, 2018 (Estimated)
Total Intended Award Amount: $615,404.00
Total Awarded Amount to Date: $615,404.00
Funds Obligated to Date: FY 2014 = $615,404.00
History of Investigator:
  • Sriram Sankaranarayanan (Principal Investigator)
    srirams@colorado.edu
  • David Bortz (Co-Principal Investigator)
  • Shalom Ruben (Co-Principal Investigator)
  • David Maahs (Co-Principal Investigator)
Recipient Sponsored Research Office: University of Colorado at Boulder
3100 MARINE ST
Boulder
CO  US  80309-0001
(303)492-6221
Sponsor Congressional District: 02
Primary Place of Performance: University of Colorado at Boulder
University of Colorado Boulder
Boulder
CO  US  80309-0430
Primary Place of Performance
Congressional District:
02
Unique Entity Identifier (UEI): SPVKK1RC2MZ3
Parent UEI:
NSF Program(s): CPS-Cyber-Physical Systems
Primary Program Source: 01001415DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 8235
Program Element Code(s): 791800
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

Title: CPS:Synergy:Collaborative Research: In-Silico Functional Verification of Artificial Pancreas Control Algorithms.

The project investigates a formal verification framework for artificial pancreas (AP) controllers that automate the delivery of insulin to patients with type-1 diabetes (T1D). AP controllers are safety critical: excessive insulin delivery can lead to serious, potentially fatal, consequences. The verification framework under development allows designers of AP controllers to check that their control algorithms will operate safely and reliably against large disturbances that include patient meals, physical activities, and sensor anomalies including noise, delays, and sensor attenuation. The intellectual merits of the project lie in the development of state-of-the-art formal verification tools, that reason over mathematical models of the closed-loop including external disturbances and insulin-glucose response. These tools perform an exhaustive exploration of the closed loop system behaviors, generating potentially adverse situations for the control algorithm under verification. In addition, automatic techniques are being investigated to help AP designers improve the control algorithm by tuning controller parameters to eliminate harmful behaviors and optimize performance. The broader significance and importance of the project are to minimize the manual testing effort for AP controllers, integrate formal tools in the certification process, and ultimately ensure the availability of safe and reliable devices to patients with type-1 diabetes. The framework is made available to researchers who are developing AP controllers to help them verify and iteratively improve their designs. The team is integrating the research into the educational mission by designing hands-on courses to train undergraduate students in the science of Cyber-Physical Systems (CPS) using the design of AP controllers as a motivating example. Furthermore, educational material that explains the basic ideas, current challenges and promises of the AP concept is being made available to a wide audience that includes patients with T1D, their families, interested students, and researchers.

The research is being carried out collaboratively by teams of experts in formal verification for Cyber-Physical Systems, control system experts with experience designing AP controllers, mathematical modeling experts, and clinical experts who have clinically evaluated AP controllers. To enable the construction of the verification framework from the current state-of-the-art verification tools, the project is addressing major research challenges, including (a) building plausible mathematical models of disturbances from available clinical datasets characterizing human meals, activity patterns, and continuous glucose sensor anomalies. The resulting models are integrated in a formal verification framework; (b) simplifying existing models of insulin glucose response using smaller but more complex delay differential models; (c) automating the process of abstracting the controller implementation for the purposes of verification; (d) producing verification results that can be interpreted by control engineers and clinical researchers without necessarily understanding formal verification techniques; and (e) partially automating the process of design improvements to potentially eliminate severe faults and improve performance. The framework is evaluated on a set of promising AP controller designs that are currently under various stages of clinical evaluation.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 12)
David M Bortz "Characteristic Roots for Two-Lag Linear Delay Differential Equations." Discrete Continuous Dynamical Systems - B , v.21 , 2016
Deshmukh, Jyotirmoy and Sankaranarayanan, Sriram "Formal Techniques for Verification and Testing of Cyber-Physical Systems" Design Automation for Cyber Physical Systems (Edited Volume) , 2019 Citation Details
Dutta, Souradeep and Kushner, Taisa and Sankaranarayanan, Sriram "Robust Data-Driven Control of Artificial Pancreas Systems Using Neural Networks" Lecture notes in computer science , v.11095 , 2018 Citation Details
Greg Forlenza, Sriram Sankaranarayanan and David Maahs "Refining the Closed Loop in the Data Age: Research-to-Practice Transitions in Diabetes Technology (Editorial)" Diabetes Technology and Therapeutics , v.17 , 2015 , p.304
Ram Das Diwakaran, Sriram Sankaranarayanan, Ashutosh Trivedi "Analyzing neighborhoods of falsifying traces in cyber-physical systems." Intl. Conference on Cyber Physical Systems , 2017 , p.109 10.1145/3055004.3055029
Souradeep Dutta and Taisa Kushner and Sriram Sankaranarayanan "Robust Data-Driven Control of Artificial Pancreas Systems using Neural Networks" Computational Methods in Systems Biology, Lecture Notes in Computer Science , v.11095 , 2018 , p.183
Sriram Sankaranarayanan, Suhas Akshar Kumar, Faye Cameron, B. Wayne Bequette, Georgios Fainekos, and David M. Maahs "Model-Based Falsification of an Artificial Pancreas Control System." ACM SIGBED Review (Special Issue on Medical Cyber Physical Systems) , v.14 , 2017 , p.24 10.1145/3076125.3076128
Taisa Kushner and David Bortz and David Maahs and Sriram Sankaranarayanan "A Data-Driven Approach to Artificial Pancreas Verification and Synthesis." International Conference on Cyber-Physical Systems , 2018 , p.242
Taisa Kushner,Sriram Sankaranarayanan, David Bortz and David Maahs "Personalized Data-Driven Verification andSynthesis for Artificial Pancreas Controller" ATTD 2018 , 2018
Xin Chen and Sriram Sankaranarayanan "Decomposed Reachability Analysis for Nonlinear Systems" IEEE Real-Time Systems Symposium , 2016 , p.13 10.1109/RTSS.2016.011
Xin Chen and Sriram Sankaranarayanan "Model Predictive Monitoring for Linear Systems" IEEE Real Time Systems Symposium (RTSS) , 2017
(Showing: 1 - 10 of 12)

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

The project studied the application of verfication approaches to  artificial pancreas controllers in collabortion with engineers who develop the control system and clinicians who study the performance of these systems on patients. During the course of this project, we applied formal specification, verification and falsficiation approaches to a variety of control systems including PID, and rule-based controllers. Our initial work used  ODE models of human physiology with parameter sets representing various patients. However, personalizing these models to individual patient physiology proved hard. In fact, human response to  insulin and behavior around meals and physical activities are highly variable depending on age, gender, weight and height, among many other factors. We  developed data-driven models and performed studies of controllers using these models. 

The project contributed to collaborative educational outcomes that include a course on closed loop medical devices developed by PI Sankaranarayanan in collaboration with other PIs including Maahs, Cameron and Dr. Forlenza at the Barbara Davis Center, UC Denver Medical school. We also presented our work at numerous conferences and research seminars at various places including  Stanford University, Barbara Davis Center for Type-1 Diabetes and 

The key outcomes of this proposal include nearly 15 research papers including papers presented at diabetes conferences such as ATTD, journal paper in Diabetes Technology & Therapeutics, engineering conferences such as CPSWeek, and venues such as EMSOFT and IEEE RTSS. One MS thesis was published under this project and currently one PhD thesis in progress will owe partial support to this grant. Awards include a best paper nomination for EMSOFT 2017 and a best clinical poster award presented to PhD student Taisa Kushner at the Barbara Davis Center research symposium. Taisa's work won a place in the final at the "three minute thesis" competition organized by CU Boulder. The project involved undergraduate researchers including Alexandra Okeson, who won best poster award at the College of Engineering undergraduate research exposition. 

 

 


Last Modified: 02/10/2019
Modified by: Sriram Sankaranarayanan

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