# Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas

^{*}

## Abstract

**:**

## 1. Introduction

## 2. AP System Architecture

#### 2.1. Components of the AP System

#### 2.2. Hardware Configurations of the AP System

#### 2.2.1. Configuration A

#### 2.2.2. Configuration B

#### 2.3. Software Architecture

#### 2.3.1. User Interface Layer

#### 2.3.2. Task Handling Layer

#### 2.3.3. Control and Safety Layer

#### 2.3.4. Operating System and Hardware Layers

## 3. Control Algorithms for the Artificial Pancreas

#### 3.1. Proportional Integral Derivative Control

_{P}, ${\mathsf{\tau}}_{I},\text{}{\mathsf{\tau}}_{D}$, denote the controller parameters. The PID update Equation (1) has to be discretized in order to be implemented in digital platforms [64,65], given by:

#### 3.2. Fuzzy Logic Control

#### 3.3. Model Predictive Control

_{i}, u

_{i}

_{+1}, …, u

_{i + Nc}

_{−1}} is obtained, only the first value u

_{i}is applied to the system, and the optimization problem is then reformulated and solved at the next time instant, when new measurement information (namely, y

_{i}

_{+1}) is available. The optimization problem that yields the optimal zone-MPC control action, described in [62,72], is given by,

_{i}is the current measurement output. With glucose excursions z

_{k}deviating from the euglycemic zone defined with the zone-excursion function Z, where,

_{p}and N

_{c}, with ${N}_{c}\le {N}_{p}$, denote the prediction and control horizons of the MPC. The successful implementation of MPC in real-time applications relies on balancing the different aspects of controller design, such as constraint satisfaction, optimality, stability and complexity reduction. The repetitive solution of the optimization problem (3) is computationally intensive and possibly prohibitive for low-memory and low-power hardware implementation. In real-time applications with hard constraints on execution time and storage capabilities, it is very challenging to compute an optimal solution in actual time. To overcome this challenge, alternative approaches that can be employed are discussed in this section.

#### 3.3.1. Explicit/Multi-Parametric MPC

#### 3.3.2. Modified On-Line MPC for Embedded Control Systems

#### 3.3.3. Optimization Algorithms and Solvers

#### 3.3.3.1. Active-Set Method

#### 3.3.3.2. Interior Point Method

#### 3.3.4. Performance Comparison

_{p}= 20, N

_{c}= 10 and (ii) N

_{p}= 12, N

_{c}= 5, to investigate the solvers’ performance for different problem sizes. The resulting quadratic program, which is to be solved every 5 min, involves 4N

_{c}+ 2(N

_{p}− N

_{c}) optimization variables and 8N

_{c}+ 4(N

_{p}− N

_{c}) inequality constraints.

## 4. Brief Overview on Design Approaches

## 5. Key Challenges of Embedded Artificial Pancreas Systems

#### 5.1. Known and Foreseeable Hazards Associated with the Operation of the AP System

#### 5.2. Communication: Security and Confidentiality

## 6. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 1.**Schematic artificial pancreas (AP) system architecture. In Configuration A (top), the control unit is an application installed on a handheld device. In Configuration B (bottom), the embedded controller is integrated into the actuating insulin pump (BG = blood glucose; CGM = continuous glucose monitoring; RF = radio frequency).

**Figure 2.**Software architecture for AP. (

**a**) Overview of the layered software architecture. The control and safety layer enclosed in the orange dashed line is enlarged and presented in (

**b**,

**c**); (

**b**) architecture with fault detection layer; and (

**c**) architecture with control and safe control layers. IOB, insulin-on-board.

**Figure 3.**Methodological framework towards an Application-Specific Integrated Circuit (ASIC) implementation.

**Figure 4.**TCP (Transmission Control Protocol) communication protocol for RPi (Raspberry Pi) and PC. RCV and SND indicate receive and send data respectively.

**Figure 5.**(Top) Comparison of glucose regulation with zone-MPC implemented on a PC, on Raspberry Pi 3 and Raspberry Pi Zero for 50-g and 70-g unannounced meal (blue, red, yellow lines). Comparison of glucose regulation with zone-MPC implemented on Raspberry Pi Zero with multiple bit precisions for quadratic programming problem (QP) solving and associated computations (yellow and purple lines). (Bottom) Comparison of optimal insulin levels computed by the different implementations.

**Figure 6.**Potential hazards in the AP system: (

**a**) controller as a smartphone app; (

**b**) controller embedded in the pump.

**Table 1.**Hardware specifications for various AP (artificial pancreas) configurations. Comparison metrics include: processing unit, memory capability, clock frequency, arithmetic logic unit (ALU) and availability of floating point units (FPU).

Hardware Specifications | ||||
---|---|---|---|---|

Configuration | (A) Smartphone | (B) Insulin Pump | ||

Processor Core | ARM Cortex-A15 | ARM Cortex-M3 | MAXQ2010 | 8-bit S08 |

Memory | 2 GB RAM | 96 KB on-chip RAM | 64 KB Flash | 2–128 KB Flash |

2 KB RAM | 0.128–12 KB RAM | |||

CPU Clock Frequency | 1.5–1.7 GHz | 72 MHz | 10 MHz | 16–50 MHz |

Integer ALU | 32-bit | 32-bit | 16-bit | 8-bit |

FPU | Optional | ✘ | ✘ | ✘ |

**Table 2.**Solver runtime performance computed for 4000 time points; t

_{aver}, t

_{max}and SD (standard deviation) are the average, the maximum and the standard deviation of the solver runtime in ms observed over the duration of the experiment; IPM (interior point method) and ASM (active-set method) are the interior point method and the active-set method, respectively, and Y/N indicate the inclusion or not of the warm-start procedure. In detail, the examined solvers are Quadprog (quadratic programming) of Matllab, qpOASES (quadratic programming online active set strategy), CVXGEN (code generator for embedded convex optimization), QPC (quadratic programming in C) using either qpas (quadratic programming active set) or qpip (quadratic programming interior point) and ECOS (embedded conic optimization solver).

Solver | Quadprog | Quadprog | Quadprog | qpOASES | qpOASES | CVXGEN | QPC (qpas) | QPC (qpip) | ECOS |
---|---|---|---|---|---|---|---|---|---|

Algorithm | IPM | ASM | ASM | ASM | ASM | IPM | ASM | IPM | IPM |

Warm-start? | N | N | Y | N | Y | N | N | N | N |

Case 1: Np = 20, Nc = 10 | |||||||||

t_{aver} (±SD) (ms) | 8.6 (±3.3) | 12.9 (±20.6) | 9.9 (±14.5) | 13.7 (±6.9) | 12.7 (±6.1) | N/A | 0.4 (±0.3) | 4.3 (±1.9) | 7.2 (±2.5) |

t_{max} (ms) | 34 | 114 | 93 | 49 | 48 | N/A | 2 | 15 | 30 |

Case 2: Np = 12, Nc = 5 | |||||||||

t_{aver} (±SD) (ms) | 7.9 (±2.8) | 6.6 (±7.6) | 5.8 (±6.0) | 2.5 (±1.4) | 2.5 (±1.3) | 3.0 (±0.7) | 0.2 (±0.15) | 1.6 (±0.7) | 6.6 (±2.3) |

t_{max} (ms) | 33 | 46 | 53 | 22 | 10 | 8 | 2 | 5 | 39 |

Solver | qpOASES | CVXGEN | ECOS | qpas | qpip |
---|---|---|---|---|---|

Code size (KB) | 835 | 1800 | 7 | 25 | 43 |

**Table 4.**Solver runtime performance computed for 4000 time points; t

_{aver}, t

_{max}and SD are the average, the maximum and the standard deviation of the solver runtime in ms observed over the duration of the experiment; the average and the standard deviation of the total energy in mAh consumed over the duration of the experiment are reported for both platforms.

Prototype Platform | Raspberry Pi Zero | Raspberry Pi 3 | ||
---|---|---|---|---|

Solver | CVXOPT | CVXPY | CVXOPT | CVXPY |

Case 1: N_{p} = 20, N_{c}= 10 | ||||

t_{aver} ± SD (ms) | 128.8 ± 14.1 | 189.7 ± 16.3 | 50.0 ± 10.2 | 73.7 ± 11.7 |

t_{max} (ms) | 196.8 | 268 | 104.8 | 143.2 |

Energy Consumed (mAh) | 39.9 ± 0.2 | 59.9 ± 0.4 | 16.5 ± 0.0 | 25.1 ± 0.1 |

Case 2: Np = 12, Nc = 5 | ||||

t_{aver} ± SD (ms) | 56.9 ± 9.2 | 104.9 ± 11.3 | 22.4 ± 7.8 | 41.2 ± 9.6 |

t_{max} (ms) | 110.3 | 269 | 40.2 | 98.2 |

Energy Consumed (mAh) | 16.5 ± 0.2 | 32.4 ± 2.0 | 6.7 ± 0.4 | 13.2 ± 0.1 |

Cause | Hazard |
---|---|

Control Unit | (1) Battery failure |

(2) No value displayed | |

(3) Over/under delivery of insulin | |

(4) Over delivery of insulin | |

Control Algorithm | (5) Numerical inaccuracies |

(6) Resource limitation-suboptimal control action | |

(7) Software aging | |

CGM (continuous glucose monitoring) | (8) No signal |

(9) Under/over reading | |

(10) No glucose display | |

(11) No communication | |

(12) Receiver displays failure | |

Insulin Pump | (13) Failure in reservoir |

(14) Failure in delivery line | |

(15) Battery failure | |

(16) Mechanical/electrical failure | |

(17) Request for new delivery while delivering previous bolus | |

(18) Insulin delivered at maximum infusion rate (60 U/h) | |

(19) Over/under/no delivery of insulin | |

User | (20) Infusion set disconnected |

(21) Over/under delivery of insulin | |

Communication | (22) No communication between CGM and transmitter |

(23) No communication between CGM and control unit | |

(24) Loss of communication between CGM and insulin pump | |

(25) No communication between control unit and insulin pump | |

Safety Layer | (26) Algorithmic fault |

(27) No detection made | |

(28) Failure to trigger an alarm | |

Handheld Device | (29) Operating system upgrade |

(30) Interference with other applications | |

(31) Cybersecurity vulnerabilities |

© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

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**MDPI and ACS Style**

Zavitsanou, S.; Chakrabarty, A.; Dassau, E.; Doyle, F.J.
Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas. *Processes* **2016**, *4*, 35.
https://doi.org/10.3390/pr4040035

**AMA Style**

Zavitsanou S, Chakrabarty A, Dassau E, Doyle FJ.
Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas. *Processes*. 2016; 4(4):35.
https://doi.org/10.3390/pr4040035

**Chicago/Turabian Style**

Zavitsanou, Stamatina, Ankush Chakrabarty, Eyal Dassau, and Francis J. Doyle.
2016. "Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas" *Processes* 4, no. 4: 35.
https://doi.org/10.3390/pr4040035