739 research outputs found
KnowSafe: Combined Knowledge and Data Driven Hazard Mitigation in Artificial Pancreas Systems
Significant progress has been made in anomaly detection and run-time
monitoring to improve the safety and security of cyber-physical systems (CPS).
However, less attention has been paid to hazard mitigation. This paper proposes
a combined knowledge and data driven approach, KnowSafe, for the design of
safety engines that can predict and mitigate safety hazards resulting from
safety-critical malicious attacks or accidental faults targeting a CPS
controller. We integrate domain-specific knowledge of safety constraints and
context-specific mitigation actions with machine learning (ML) techniques to
estimate system trajectories in the far and near future, infer potential
hazards, and generate optimal corrective actions to keep the system safe.
Experimental evaluation on two realistic closed-loop testbeds for artificial
pancreas systems (APS) and a real-world clinical trial dataset for diabetes
treatment demonstrates that KnowSafe outperforms the state-of-the-art by
achieving higher accuracy in predicting system state trajectories and potential
hazards, a low false positive rate, and no false negatives. It also maintains
the safe operation of the simulated APS despite faults or attacks without
introducing any new hazards, with a hazard mitigation success rate of 92.8%,
which is at least 76% higher than solely rule-based (50.9%) and data-driven
(52.7%) methods.Comment: 16 pages, 10 figures, 9 tables, submitted to the IEEE for possible
publicatio
Challenges and Opportunities in Design of Control Algorithm for Artificial Pancreas
With discovery of the insulin, Type-1 diabetes converted from a fatal and acute to a chronic disease which includes micro-vascular complications which range from Kidney disease to stroke and micro-vascular complications such as retinopathy, nephropathy and neuropathy. Artificial pancreas is a solution to improve the quality of life for people with this very fast growing disease in the world and to reduce the costs. Despite technological advances e.g., in subcutaneous sensors and actuators for insulin injection, modeling of blood glucose dynamics and control algorithms still need significant improvement. In this paper, we investigate challenges and opportunities for development of efficient algorithm for designing robust artificial pancreas. We discuss the state of the art and summarize clinical and in silico assessment results. We contrast conventional integer order system approach with a newly proposed fractal control and summarize its benefits
Evidence-based Development of Trustworthy Mobile Medical Apps
abstract: Widespread adoption of smartphone based Mobile Medical Apps (MMAs) is opening new avenues for innovation, bringing MMAs to the forefront of low cost healthcare delivery. These apps often control human physiology and work on sensitive data. Thus it is necessary to have evidences of their trustworthiness i.e. maintaining privacy of health data, long term operation of wearable sensors and ensuring no harm to the user before actual marketing. Traditionally, clinical studies are used to validate the trustworthiness of medical systems. However, they can take long time and could potentially harm the user. Such evidences can be generated using simulations and mathematical analysis. These methods involve estimating the MMA interactions with human physiology. However, the nonlinear nature of human physiology makes the estimation challenging.
This research analyzes and develops MMA software while considering its interactions with human physiology to assure trustworthiness. A novel app development methodology is used to objectively evaluate trustworthiness of a MMA by generating evidences using automatic techniques. It involves developing the Health-Dev β tool to generate a) evidences of trustworthiness of MMAs and b) requirements assured code generation for vulnerable components of the MMA without hindering the app development process. In this method, all requests from MMAs pass through a trustworthy entity, Trustworthy Data Manager which checks if the app request satisfies the MMA requirements. This method is intended to expedite the design to marketing process of MMAs. The objectives of this research is to develop models, tools and theory for evidence generation and can be divided into the following themes:
• Sustainable design configuration estimation of MMAs: Developing an optimization framework which can generate sustainable and safe sensor configuration while considering interactions of the MMA with the environment.
• Evidence generation using simulation and formal methods: Developing models and tools to verify safety properties of the MMA design to ensure no harm to the human physiology.
• Automatic code generation for MMAs: Investigating methods for automatically
• Performance analysis of trustworthy data manager: Evaluating response time generating trustworthy software for vulnerable components of a MMA and evidences.performance of trustworthy data manager under interactions from non-MMA smartphone apps.Dissertation/ThesisDoctoral Dissertation Computer Science 201
High Fidelity Fast Simulation of Human in the Loop Human in the Plant (HIL-HIP) Systems
Non-linearities in simulation arise from the time variance in wireless mobile
networks when integrated with human in the loop, human in the plant (HIL-HIP)
physical systems under dynamic contexts, leading to simulation slowdown. Time
variance is handled by deriving a series of piece wise linear time invariant
simulations (PLIS) in intervals, which are then concatenated in time domain. In
this paper, we conduct a formal analysis of the impact of discretizing
time-varying components in wireless network-controlled HIL-HIP systems on
simulation accuracy and speedup, and evaluate trade-offs with reliable
guarantees. We develop an accurate simulation framework for an artificial
pancreas wireless network system that controls blood glucose in Type 1 Diabetes
patients with time varying properties such as physiological changes associated
with psychological stress and meal patterns. PLIS approach achieves accurate
simulation with greater than 2.1 times speedup than a non-linear system
simulation for the given dataset.Comment: To appear in ACM MSWIM 202
Design and Validation of an Open-Source Closed-Loop Testbed for Artificial Pancreas Systems
The development of a fully autonomous artificial pancreas system (APS) to
independently regulate the glucose levels of a patient with Type 1 diabetes has
been a long-standing goal of diabetes research. A significant barrier to
progress is the difficulty of testing new control algorithms and safety
features, since clinical trials are time- and resource-intensive. To facilitate
ease of validation, we propose an open-source APS testbed by integrating APS
controllers with two state-of-the-art glucose simulators and a novel fault
injection engine. The testbed is able to reproduce the blood glucose
trajectories of real patients from a clinical trial conducted over six months.
We evaluate the performance of two closed-loop control algorithms (OpenAPS and
Basal Bolus) using the testbed and find that more advanced control algorithms
are able to keep blood glucose in a safe region 93.49% and 79.46% of the time
on average, compared with 66.18% of the time for the clinical trial. The fault
injection engine simulates the real recalls and adverse events reported to the
U.S. Food and Drug Administration (FDA) and demonstrates the resilience of the
controller in hazardous conditions. We used the testbed to generate 2.5 years
of synthetic data representing 20 different patient profiles with realistic
adverse event scenarios, which would have been expensive and risky to collect
in a clinical trial. The proposed testbed is a valid tool that can be used by
the research community to demonstrate the effectiveness of different control
algorithms and safety features for APS.Comment: 12 pages, 12 figures, to appear in the IEEE/ACM International
Conference on Connected Health: Applications, Systems and Engineering
Technologies (CHASE), 202
Towards a Model-Based Meal Detector for Type I Diabetics
Blood glucose management systems are an important class of Medical Cyber-Physical Systems that provide vital everyday decision support service to diabetics. An artificial pancreas, which integrates a continuous glucose monitor, a wearable insulin pump, and control algorithms running on embedded computing devices, can significantly improve the quality of life for millions of Type 1 diabetics. A primary problem in the development of an artificial pancreas is the accurate detection and estimation of meal carbohydrates, which cause significant glucose system disturbances. Meal carbohydrate detection is challenging since post-meal glucose responses greatly depend on patient-specific physiology and meal composition.
In this paper, we develop a novel meal-time detector that leverages a linearized physiological model to realize a (nearly) constant false alarm rate (CFAR) performance despite unknown model parameters and uncertain meal inputs. Insilico evaluations using 10, 000 virtual subjects on an FDA-accepted maximal physiological model illustrate that the proposed CFAR meal detector significantly outperforms a current state-of-the-art meal detector that utilizes a voting scheme based on rate-of-change (RoC) measures. The proposed detector achieves 99.6% correct detection rate while averaging one false alarm every 24 days (a 1.4% false alarm rate), which represents an 84% reduction in false alarms and a 95% reduction in missed alarms when compared to the RoC approach
Digital-Twins towards Cyber-Physical Systems: A Brief Survey
Cyber-Physical Systems (CPS) are integrations of computation and physical processes. Physical processes are monitored and controlled by embedded computers and networks, which frequently have feedback loops where physical processes affect computations and vice versa. To ease the analysis of a system, the costly physical plants can be replaced by the high-fidelity virtual models that provide a framework for Digital-Twins (DT). This paper aims to briefly review the state-of-the-art and recent developments in DT and CPS. Three main components in CPS, including communication, control, and computation, are reviewed. Besides, the main tools and methodologies required for implementing practical DT are discussed by following the main applications of DT in the fourth industrial revolution through aspects of smart manufacturing, sixth wireless generation (6G), health, production, energy, and so on. Finally, the main limitations and ideas for future remarks are talked about followed by a short guideline for real-world application of DT towards CPS
Medical Device Artificial Intelligence: The New Tort Frontier
The medical device industry and new technology start-ups have dramatically increased investment in artificial intelligence (AI) applications, including diagnostic tools and AI-enabled devices. These technologies have been positioned to reduce climbing health costs while simultaneously improving health outcomes. Technologies like AI-enabled surgical robots, AI-enabled insulin pumps, and cancer detection applications hold tremendous promise, yet without appropriate oversight, they will likely pose major safety issues. While preventative safety measures may reduce risk to patients using these technologies, effective regulatory-tort regimes also permit recovery when preventative solutions are insufficient.
The Food and Drug Administration (FDA), the administrative agency responsible for overseeing the safety and efficacy of medical devices, has not effectively addressed AI system safety issues for its clearance processes. If the FDA cannot reasonably reduce the risk of injury for AI-enabled medical devices, injured patients should be able to rely on ex post recovery options, as in products liability cases. However, the Medical Device Amendments Act (MDA) of 1976 introduced an express preemption clause that the U.S. Supreme Court has interpreted to nearly foreclose liability claims, based almost completely on the comprehensiveness of FDA clearance review processes. At its inception, MDA preemption aimed to balance consumer interests in safe medical devices with efficient, consistent regulation to promote innovation and reduce costs.
Although preemption remains an important mechanism for balancing injury risks with device availability, the introduction of AI software dramatically changes the risk profile for medical devices. Due to the inherent opacity and changeability of AI algorithms powering AI machines, it is nearly impossible to predict all potential safety hazards a faulty AI system might pose to patients. This Article identifies key preemption issues for AI machines as they affect ex ante and ex post regulatory-tort allocation, including actual FDA review for parallel claims, bifurcation of software and device reviews, and dynamics of the technology itself that may enable plaintiffs to avoid preemption. This Author then recommends an alternative conception of the regulatory-tort allocation for AI machines that will create a more comprehensive and complementary safety and compensatory model
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