DAMON: A Data Authenticity Monitoring System for Diabetes Management

We present DAMON, a data authenticity monitoring system for use in an Internet of Medical Things (IoMT) system assembled to treat Type 1 Diabetes (T1D). We describe the use of Signal Temporal Logic (STL) for specifying and monitoring a range of system properties relevant to T1D treatment, including constraints on glycemic variability and insulin delivery. We perform retrospective analysis of posterior probabilities of multiple meal hypotheses to detect suspicious meal events. Using a corpus of clinical study data, we provide experimental results demonstrating the detection of system events indicative of compromised data authenticity.

[1]  Garvit Juniwal,et al.  Robust online monitoring of signal temporal logic , 2015, Formal Methods in System Design.

[2]  Alexandre Donzé,et al.  Breach, A Toolbox for Verification and Parameter Synthesis of Hybrid Systems , 2010, CAV.

[3]  J. Timothy Nichols,et al.  User authentication in medical systems , 2000 .

[4]  Sriram Sankaranarayanan,et al.  Towards a Verified Artificial Pancreas: Challenges and Solutions for Runtime Verification , 2015, RV.

[5]  Ransford Henry Botwey,et al.  Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study , 2016, Journal of medical Internet research.

[6]  Dejan Nickovic,et al.  Monitoring Temporal Properties of Continuous Signals , 2004, FORMATS/FTRTFT.

[7]  Xiaojiang Du,et al.  Patient Infusion Pattern based Access Control Schemes for Wireless Insulin Pump System , 2015, IEEE Transactions on Parallel and Distributed Systems.

[8]  M. Phillip,et al.  Effect of Continuous Glucose Monitoring on Hypoglycemia in Type 1 Diabetes , 2011, Diabetes Care.

[9]  Y. Z. Ider,et al.  Quantitative estimation of insulin sensitivity. , 1979, The American journal of physiology.

[10]  S. Genuth,et al.  The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. , 1993, The New England journal of medicine.

[11]  N. Nagelkerke,et al.  Accurate Carbohydrate Counting Is an Important Determinant of Postprandial Glycemia in Children and Adolescents With Type 1 Diabetes on Insulin Pump Therapy , 2017, Journal of diabetes science and technology.

[12]  Manfred Schubert-Zsilavecz,et al.  Study on the dosing accuracy of commonly used disposable insulin pens. , 2012, Diabetes technology & therapeutics.

[13]  John Walsh,et al.  Insulin Pump and CGM Usage in the United States and Germany , 2015, Journal of diabetes science and technology.

[14]  Stephen D. Patek.  Open-loop feedback control under multiple disturbance function hypotheses , 2010, 49th IEEE Conference on Decision and Control (CDC).

[15]  J. Pickup,et al.  Are insulin pumps underutilized in type 1 diabetes? Yes. , 2006, Diabetes care.

[16]  George J. Pappas,et al.  Robustness of temporal logic specifications for continuous-time signals , 2009, Theor. Comput. Sci..

[17]  T. Kawamura,et al.  The factors affecting on estimation of carbohydrate content of meals in carbohydrate counting , 2015, Clinical pediatric endocrinology : case reports and clinical investigations : official journal of the Japanese Society for Pediatric Endocrinology.

[18]  Alberto L. Sangiovanni-Vincentelli,et al.  Model Predictive Control for Signal Temporal Logic Specification , 2017, ArXiv.

[19]  Ashish Tiwari,et al.  TeLEx: Passive STL Learning Using Only Positive Examples , 2017, RV.

[20]  Sriram Sankaranarayanan,et al.  S-TaLiRo: A Tool for Temporal Logic Falsification for Hybrid Systems , 2011, TACAS.

[21]  Oded Maler,et al.  Robust Satisfaction of Temporal Logic over Real-Valued Signals , 2010, FORMATS.