Abstract
Significant increases in processing power, coupled with the miniaturization of processing units operating at low power levels, has motivated the embedding of modern control systems into medical devices. The design of such embedded decision-making strategies for medical applications is driven by multiple crucial factors, such as: (i) guaranteed safety in the presence of exogenous disturbances and unexpected system failures; (ii) constraints on computing resources; (iii) portability and longevity in terms of size and power consumption; and (iv) constraints on manufacturing and maintenance costs. Embedded control systems are especially compelling in the context of modern artificial pancreas systems (AP) used in glucose regulation for patients with type 1 diabetes mellitus (T1DM). Herein, a review of potential embedded control strategies that can be leveraged in a fully-automated and portable AP is presented. Amongst competing controllers, emphasis is provided on model predictive control (MPC), since it has been established as a very promising control strategy for glucose regulation using the AP. Challenges involved in the design, implementation and validation of safety-critical embedded model predictive controllers for the AP application are discussed in detail. Additionally, the computational expenditure inherent to MPC strategies is investigated, and a comparative study of runtime performances and storage requirements among modern quadratic programming solvers is reported for a desktop environment and a prototype hardware platform.
Highlights
Embedded electronics are widely incorporated in medical devices for diagnosis, prognosis and monitoring of a disease
The exact specifications of the microcontrollers used in commercial insulin pumps are not openly available, the standard requirements for portable medical devices are met with the low-power microcontrollers, such as STM32 [38], which uses a Cortex M3 processor core based on the 32-bit
Embedded control for medical applications is an emerging field that is driven by the need to develop wearable medical devices that actively control the patient’s treatment and health management
Summary
Embedded electronics are widely incorporated in medical devices for diagnosis, prognosis and monitoring of a disease. Processes 2016, 4, 35 execute decision-making strategies based on the acquired measurement information Additional design specifications, such as, for example, (i) runtime constraints imposed by the implementation platform;. The most advanced treatment of T1DM employs a continuous glucose monitoring system (CGM) that takes measurements of glucose concentration at regular intervals These CGM measurements are leveraged by a control algorithm in the AP, which (without the patient’s active involvement) computes an appropriate magnitude of insulin infusion. The control algorithms for the AP are implemented on CPU-based devices, such as PCs, laptops and tablets, with a recent shift towards smaller processing units, such as those found in smartphones.
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