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Publication Computer Aided Clinical Trials for Implantable Cardiac Devices(2018-07-12) Jang, Kuk Jin; Weimer, James; Abbas, Houssam; Jiang, Zhihao; Liang, Jackson; Dixit, Sanjay; Mangharam, RahulIn this paper we aim to answer the question, ``How can modeling and simulation of physiological systems be used to evaluate life-critical implantable medical devices?'' Clinical trials for medical devices are becoming increasingly inefficient as they take several years to conduct, at very high cost and suffer from high rates of failure. For example, the Rhythm ID Goes Head-to-head Trial (RIGHT) sought to evaluate the performance of two arrhythmia discriminator algorithms for implantable cardioverter defibrillators, Vitality 2 vs. Medtronic, in terms of time-to-first inappropriate therapy, but concluded with results contrary to the initial hypothesis - after 5 years, 2,000+ patients and at considerable ethical and monetary cost. In this paper, we describe the design and performance of a computer-aided clinical trial (CACT) for Implantable Cardiac Devices where previous trial information, real patient data and closed-loop device models are effectively used to evaluate the trial with high confidence. We formulate the CACT in the context of RIGHT using a Bayesian statistical framework. We define a hierarchical model of the virtual cohort generated from a physiological model which captures the uncertainty in the parameters and allows for the systematic incorporation of information available at the design of the trial. With this formulation, the CACT estimates the inappropriate therapy rate of Vitality 2 compared to Medtronic as 33.22% vs 15.62% (pPublication Heart-on-a-Chip: A Closed-loop Testing Platform for Implantable Pacemakers(2014-07-02) Jiang, Zhihao; Radhakrishnan, Sriram; Sampath, Varun; Sarode, Shilpa; Mangharam, RahulImplantable cardiac pacemakers restore normal heart rhythm by delivering external electrical pacing to the heart. The pacemaker software is life-critical as the timing of the pulses determine its ability to control the heart rate. Recalls due to software issues have been on the rise with the increasing complexity of pacing algorithms. Open-loop testing remains the primary approach to evaluate the safety of pacemaker software. While this tests how the pacemaker responds to stimulus, it cannot reveal pacemaker malfunctions which drive the heart into an unsafe state over multiple cycles. To evaluate the safety and efficacy of pacemaker software we have developed a heart model to generate different heart conditions and interact with real pacemakers. In this paper, we introduce the closed-loop testing platform which consists of a programmable hardware implementation of the heart that can interact with a commercial pacemaker in closed-loop. The heart-on-a-chip implementation is automatically generated from the Virtual Heart Model in Simulink which models different heart conditions. We describe a case study of Endless Loop Tachycardia to demonstrate potential closed-loop pacemaker malfunctions which inappropriately increase the heart rate. The test platform is part of our model-based design framework for verification and testing of medical devices with the patient--in-the-loop.Publication Model-Based Conformance Testing for Implantable Pacemakers(2013-07-31) Chen, George; Jiang, Zhihao; Mangharam, RahulPublication AUTOPLUG: An Architecture for Remote Electronic Controller Unit Diagnostics in Automotive Systems(2012-01-01) Pant, Yash Vardhan; Pajic, Miroslav; Mangharam, RahulIn 2010, over 20.3 million vehicles were recalled. Software issues related to automotive controls such as cruise control, anti-lock braking system, traction control and stability control, account for an increasingly large percentage of the overall vehicles recalled. There is a need for new and scalable methods to evaluate automotive controls in a realistic and open setting. We have developed AutoPlug, an automotive Electronic Controller Unit (ECU) architecture between the vehicle and a Remote Diagnostics Center to diagnose, test, update and verify controls software. Within the vehicle, we evaluate observerbased runtime diagnostic schemes and introduce a framework for remote management of vehicle recalls. The diagnostics scheme deals with both real-time and non-real time faults, and we introduce a decision function to detect and isolate faults in a system with modeling uncertainties. We also evaluate the applicability of “Opportunistic Diagnostics”, where the observerbased diagnostics are scheduled in the ECU’s RTOS only when there is slack available in the system. This aperiodic diagnostics scheme performs similar to the standard, periodic diagnostics scheme under reasonable assumptions. This approach works on existing ECUs and does not interfere with current task sets. The overall framework integrates in-vehicle and remote diagnostics and serves to make vehicle recalls management a less reactive and cost-intensive procedure.Publication Data-Driven Modeling, Control and Tools for Cyber-Physical Energy Systems(2016-04-01) Behl, Madhur; Jain, Achin; Mangharam, RahulDemand response (DR) is becoming increasingly important as the volatility on the grid continues to increase. Current DR approaches are completely manual and rule-based or in- volve deriving first principles based models which are ex- tremely cost and time prohibitive to build. We consider the problem of data-driven end-user DR for large buildings which involves predicting the demand response baseline, evaluating fixed rule based DR strategies and synthesizing DR control actions. We provide a model based control with regression trees algorithm (mbCRT), which allows us to perform closed- loop control for DR strategy synthesis for large commercial buildings. Our data-driven control synthesis algorithm out- performs rule-based DR by 17% for a large DoE commercial reference building and leads to a curtailment of 380kW and over $45, 000 in savings. Our methods have been integrated into an open source tool called DR-Advisor, which acts as a recommender system for the building’s facilities manager and provides suitable control actions to meet the desired load curtailment while maintaining operations and maximizing the economic reward. DR-Advisor achieves 92.8% to 98.9% pre- diction accuracy for 8 buildings on Penn’s campus. We com- pare DR-Advisor with other data driven methods and rank 2nd on ASHRAE’s benchmarking data-set for energy predic- tion.Publication Cyber-Physical Modeling of Implantable Cardiac Medical Devices(2011-12-29) Jiang, Zhihao; Pajic, Miroslav; Mangharam, RahulThe design of bug-free and safe medical device software is challenging, especially in complex implantable devices that control and actuate organs in unanticipated contexts. Safety recalls of pacemakers and implantable cardioverter defibrillators between 1990 and 2000 affected over 600,000 devices. Of these, 200,000 or 41%, were due to firmware issues and their effect continues to increase in frequency. There is currently no formal methodology or open experimental platform to test and verify the correct operation of medical device software within the closed-loop context of the patient. To this effect, a real-time Virtual Heart Model (VHM) has been developed to model the electrophysiological operation of the functioning and malfunctioning (i.e., during arrhythmia) heart. By extracting the timing properties of the heart and pacemaker device, we present a methodology to construct a timed-automata model for functional and formal testing and verification of the closed-loop system. The VHM's capability of generating clinically-relevant response has been validated for a variety of common arrhythmias. Based on a set of requirements, we describe a closed-loop testing environment that allows for interactive and physiologically relevant model-based test generation for basic pacemaker device operations such as maintaining the heart rate, atrial-ventricle synchrony and complex conditions such as pacemaker-mediated tachycardia. This system is a step toward a testing and verification approach for medical cyber-physical systems with the patient-in-the-loop.Publication Computer-Aided Design for Safe Autonomous Vehicles(2017-05-01) O'Kelly, Matthew; Abbas, Houssam; Mangharam, RahulThis paper details the design of an autonomous vehicle CAD toolchain, which captures formal descriptions of driving scenarios in order to develop a safety case for an autonomous vehicle (AV). Rather than focus on a particular component of the AV, like adaptive cruise control, the toolchain models the end-to-end dynamics of the AV in a formal way suitable for testing and verification. First, a domain-specific language capable of describing the scenarios that occur in the day-to-day operation of an AV is defined. The language allows the description and composition of traffic participants, and the specification of formal correctness requirements. A scenario described in this language is an executable that can be processed by a specification-guided automated test generator (bug hunting), and by an exhaustive reachability tool. The toolchain allows the user to exploit and integrate the strengths of both testing and reachability, in a way not possible when each is run alone. Finally, given a particular execution of the scenario that violates the requirements, a visualization tool can display this counter-example and generate labeled sensor data. The effectiveness of the approach is demonstrated on five autonomous driving scenarios drawn from a collection of 36 scenarios that account for over 95% of accidents nationwide. These case studies demonstrate robustness-guided verification heuristics to reduce analysis time, counterexample visualization for identifying controller bugs in both the discrete decision logic and low-level analog (continuous) dynamics, and identification of modeling errors that lead to unrealistic environment behavior.Publication Tech Report: Robust Model Predictive Control for Non-Linear Systems with Input and State Constraints Via Feedback Linearization(2016-03-15) Pant, Yash Vardhan; Abbas, Houssam; Mangharam, RahulRobust predictive control of non-linear systems under state estimation errors and input and state constraints is a challenging problem, and solutions to it have generally involved solving computationally hard non-linear optimizations. Feedback linearization has reduced the computational burden, but has not yet been solved for robust model predictive control under estimation errors and constraints. In this paper, we solve this problem of robust control of a non-linear system under bounded state estimation errors and input and state constraints using feedback linearization. We do so by developing robust constraints on the feedback linearized system such that the non-linear system respects its constraints. These constraints are computed at run-time using online reachability, and are linear in the optimization variables, resulting in a Quadratic Program with linear constraints. We also provide robust feasibility, recursive feasibility and stability results for our control algorithm. We evaluate our approach on two systems to show its applicability and performancePublication A novel programming language to reduce energy consumption by arrhythmia monitoring algorithms in implantable cardioverter-defibrillators(2018-05-09) Abbas, Houssam; Mamouras, Konstantinos; Rodionova, Alena; Liang, Jackson; Rajeev, Alur; Dixit, Sanjay; Mangharam, RahulPublication Evaluation of DR-Advisor on the ASHRAE Great Energy Predictor Shootout Challenge(2014-07-24) Behl, Madhur; Mangharam, RahulThis paper describes the evaluation of DR-Advisor algorithms on ''The Great Energy Predictor Shootout - The First Building Data Analysis and Prediction Competition'' held in 1993-94 by ASHRAE.