Improve Safety and Reliability of Medical Devices with ...€¦ · Improve Safety and Reliability of Medical Devices with Modeling and Simulation ... (OTC) ** Economist June ... Alstom

© 2014 The MathWorks, Inc.

Improve Safety and Reliability of Medical

Devices with Modeling and Simulation

Arvind Ananthan

Medical Devices Industry ManagerMathWorksNatick, USA

“Modeling and simulation (M&S)

is getting information about

how something will behave

without actually testing it in real life.” – Wikipedia

Software in Medical Devices

Complex software increasing rapidly in Medical Devices

* LOC – Lines of Code.

Source: Economist June 2012 article “When code can kill or cure”. http://www.economist.com/node/21556098

80,000 LOC*

170,000 LOC*

7 Million LOC*

Apps – software is a medical device

http://www.economist.com/node/21556098

5

Medical Device Recalls1999 – 2005

“1 in 3 of all software-based medical devices sold in

America between 1999 and 2005 had been recalled for

software failures”**

Frequent Devices for Class 1 Recall

– Infusion Pumps

– AED

– Continuous Ventilators

– Blood Glucose Monitors (OTC)

** Economist June 2012 article “When code can kill or cure”, referring to study done by University of Patras in Greece on Medical Device recalls

6

FDA Recalls

# of Recalls doubled since 2003

Increasing # of Class 1 and 2 recalls

Source: Medical Device Recall Report FY 2003-FY2012, CDRH, Office of Compliance, FDA

8

Recall Cause Categories

Majority of Recall Reasons related to 820.30 Design

Controls

– Design and revision of that design during the lifetime of the

device

9

Electrical

Components

EDA

Mechanical

Components

MCAD/

MCAE

Embeddable

Algorithms

Algorithm

Design

Traditional Development Process

SPECIFICATIONS

DESIGN AND IMPLEMENTATION

RESEARCH REQUIREMENTS

Embedded

Software

C/C++

INTEGRATION AND TEST

Electrical

Components

EDA

Mechanical

Components

MCAD/

MCAE

Embeddable

Algorithms

Algorithm

Design


Embedded

Software

C/C++

10

Electrical

Components

EDA

Mechanical

Components

MCAD/

MCAE

Embeddable

Algorithms

Algorithm

Design

Problems in Traditional Development

SPECIFICATIONS



Embedded

Software

C/C++

Paper Specifications

Incomplete, easy to misinterpret,

and often out-of-date

Physical Prototypes

Expensive!

Manual Coding

Expensive, time consuming, and

buggy!

Requirement Documents

Difficult to analyze and maintain

INTEGRATION AND TEST

Traditional Testing

Specification and design issues

found late in project

11

Late Errors Cost More

Requirements Design Coding Integration & Testing

Relative cost

to fix a

software error

Project phase where error is fixed

Type of error:

- requirements

- design

- coding

Source: Return on Investment for Independent Verification & Validation, NASA, 2004

12

Solutions1. Multi-domain System Modeling

13


2. Model-Based Design

14


Modeling

Building Modeling Solid ModelingMulti-Domain

System Modeling

Multi-domain System Modeling6 Modeling Domains

CONTINUOUS-TIME MODELSDynamic systems, Plant models, Controller

models

DISCRETE-TIME MODELSDigital control, DSP, Image/video

DISCRETE-EVENT MODELSArchitecture, Latency, Resource, Performance

modeling

STATE MACHINE MODELSControl logic, Mode logic

PHYSICAL MODELSElectronics, Mechanics, Hydraulics, Thermal

TEXT-BASED MODELS

17

Building a Robotic Surgical Arm

How a differential equation becomes a moving robot

arm

18

What makes a robot arm move?

Controller code

19

How to design and build a robot arm?

If possible, break down a big problem into “smaller”(i.e. more manageable) problems

*** Use a “divide and conquer” approach ***

20

So, where do we really start?

If possible, break down a big problem into “smaller”(i.e. more manageable) problems

*** Use a “divide and conquer” approach ***

21


Understand the underlying mathematics/physics of the problem

q

r

X

Y

22



*** Gain insight into the problem ***

q

r

X

Y

ZZ

mg

CM

23

Now we need pen and paper…


*** Gain insight into the problem ***

q

r

X

Y

ZZ

mg

mgcos(q)

CM

24

Or, we can use the Symbolic Math Toolbox

notebook interface…

• To help us derive the dynamic equations analytically

• To find analytic (when possible) and/or numerical solutions to the problem

26

Or, we can use Simulink to build a graphical

representation of the equation…

q

r

X

Y

ZZ

mg

mgcos(q)

CM

Dynamic differential equation for an ideal single link

28

Now, lets return to our original problem…

We need to “extend” the approach

29



X

Y

q1

q2

r1

r2

30



X

Y

q1

q2

r1

r2

m2g

CM2

m1g

CM1

31

X

Y

q1

q2

r1

r2

m2g

CM2

m1g

CM1

And quickly, things start to get ugly…


32

X

Y

q1

q2

r1

r2

m2g

CM2

m1g

CM1

We can solve this problem too…

In general, more complex equations will require more complex solution models

34

But, there HAS to be a more efficient way of

doing this…

Often times, it helps looking at a problem from a slightly different perspective

*** i.e. any mechanism can be defined as a combination of rigid bodies and joints ***

GROUND

BODY1

BODY2

Rotational

Joint

Rotational

Joint

X

Y

q1

q2

r1

r2

35

X

Y

q1

q2

r1

r2

We can model the dynamics of 3D mechanisms…

Using the appropriate tool for the job can sometimes make our lives much easier

GROUND

BODY1

BODY2

Rotational

Joint

Rotational

Joint

36

And manually create and combine any number

of bodies and joints…

SimMechanics will allow us to easily extend our 3D mechanical model

37

Or, we can import the entire mechanical

design directly from a CAD tool…

The translator automatically generates the SimMechanics model using CAD information

SolidWorks Model

SimMechanics Model

38

Now, for our robot to be of any use, it has to be

able to move…

We can include the effects of actuator and sensor dynamics in the simulation model

39

So, “something” has to actuate those

mechanical joints…

First Principles Equations

Simscape Components

Advanced Components Libraries

Take advantage of the flexibility provided by Simscape and the advanced physical component libraries in Simulink to create models for all kinds of actuators and sensors

40

We can balance model fidelity and simulation

speed based on our needs…

Easily compare results and control the overall level of detail in your simulation model by using configurable subsystems or switching between subsystem variants

Average voltage H-bridge amplifier model Transistor level detail H-bridge amplifier model

41

If needed, we can quickly validate our

component models using test data...

Use Simulink Design Optimization to automatically tune model parameters by taking advantage of the capabilities in the MATLAB Optimization Toolbox directly in Simulink

R L J K B

3 0.01 0.01 0.02 0.5

R = Resistance

L = Inductance

J = Inertia

B = Friction

K = Back EMF Constant

R L J K B

4.03 1e-4 0.11 0.45 1.07

42

We can store our customized component

models in libraries for easy reuse…

Develop component models and build your own custom project libraries for future use and/or sharing with other members of your team

43

So far, we have built a dynamic model of our

plant…

44

And added the actuators and sensors…

motor

voltages

joint

angles

actuators sensorsplant

45

And added the appropriate actuators and

sensors…

motor

voltages

joint

angles

actuators sensorsplant

46

Now it is time to add a controller and “close the

loop”…

reference+

-

controller

Modeling both, plant and controller in a single environment allows us to better understand and optimize the performance of the entire system

S1 S2

S3

47

And we can test and verify the overall system

performance in simulation…

Generate a battery of test vector arrays and verify the controller design against the performance specifications given for the system

reference+

-

controller

S1 S2

S3

49

医療・介護機器の制御システム設計～モデルベースデザインにトライ～

コントローラ

制御異常

停止

PID

PID

PID

アクチュエータセンサプラント

制御電気機械

＋

－

人間

指令値制御量

概要：ロボットアームのプラントモデリング、コントローラ設計講演者：福井慶一セッション番号： E4

会場： E

時間： 16:20 – 17:10

50

Modeling Blood Flow

in Capillary Networksat Draper Labs, Cambridge, MA

Simulating physiological network system for

synthetic organ development

51

Vasculature Modeling for MEMS fabrication

Microfluidics simulation to develop Kidney/Lungs

52

Blood Capillary Network Model

54

Whole Blood Testing

55

Mock Circulatory Loop (MCL) Model

for Testing Heart Pumps (or VADs)

56

Blood Pump

Compliance Chamber

Peripheral Resistance

Automating the Mock Circulatory Loop

57

Automating the Mock Circulatory Loop

Blood Pump Compliance Chamber

Peripheral Resistance

58

Read Full Technical Article

59


2. Model-Based Design

60

Model-Based Design

Process

1. Research

2. Requirements and

Specifications

3. Design

4. Implementation

5. Test and Verification

Traditional System

Development Process

“Waterfall”

61

INTEGRATION

IMPLEMENTATION

DESIGN

Environment Models

Physical Component Models

Algorithms

Structured

TextVHDL, VerilogC, C++

TE

ST

& V

ER

IFIC

AT

ION


PLC, PACMCU DSP FPGA ASIC

Model-Based Design

WorkflowTraditional System

Development Process

“Waterfall”

Model specification

Requirements in the model

Design is iterative

Code is generated automatically

Testing and verification is continuous

62

Artificial Pancreas ProjectDeveloping the next generation of closed-loop

insulin infusion system

63

Carbohydrate Metabolism with T1DM

Time

Blo

od G

lucose L

evel

64

Type 1 Diabetes with Insulin Therapy

Time

Blo

od G

lucose L

evel

Bolus Bolus

65

Glycemic System Model & Insulin Pump

Modeling Device and Patient

66

DESIGN

RESEARCH

Partitioning

Physical Components

Environment Models

IMPLEMENTATION

MCU DSP FPGA ASIC

Structured

TextVHDL, Verilog

PLC

Requirements: Linking and Tracing

Tests

Word,

DOORS,

etc.

Algorithms

REQUIREMENTS

C, C++

Code

Designs

67

INTEGRATION

IMPLEMENTATION

MCU DSP FPGA ASIC

Structured

TextVHDL, VerilogC, C++

PLC

Model-Based Design – Self Documenting

DESIGN


Environment Models

Physical Components

Algorithms

TE

ST

AN

D V

ER

IFIC

AT

ION

Models Requirements

Traceability Report

Conformance

Report

System Test

Report

Model Coverage

ReportProperty Proving

ReportAnnotated

source code

Model/Code

Traceability Report

MISRA & JSF++

XML Report

PolySpace code

correctness data

Test Generation

Report

Reports &

Documents

Custom Reports with

MATLAB and Simulink Report Generator

68

Model-Based Design mapping with IEC-62304

70

IEC 61508 TÜV Certification of

MathWorks Tools

Certificate based on:

– Focused audit by TÜV of The MathWorks development and quality assurance processes

– Review by TÜV of MathWorks document describing example workflow for verification and validation of models and generated code

– Test cases and test procedures to support tool validation

Certificate report

Workflow description

71

Model-Based Design in Medical Devices

Smart Digital RF Power Subsystems for MRI Systems

Design issues resolved early in development

Tradeoffs rapidly assessed and implemented

Process consistency and predictability improved

Accelerate FPGA Development for IVUS

System constructs a 3-D model of the area around the car

Uses 2-D images generated by a single side-view camera

Image processing algorithms developed 3-4 times faster than in C or C++

Software for Emergency Ventilators

Code development and reviews accelerated by 50%

Dozens of design alternatives explored

60% of core design reused

Image processing algorithms for retinal prosthesis

Development time reduced from months to weeks

DSP deployment streamlined

Patient testing improved

Hearing Aids and Implants

Real-time prototypes updated in minutes, not days

80% of software libraries reused in platform

Software quality improved

Philips

Infraredx

Weinmann

Sonova/

PHONAK

Doheny Eye

Inst.(Second Sight)

72

Model-Based Design

– where is it being used?

Weinmann Medical Germany

Transport ventilator

GM USA

Hybrid Powertrain

MTU GermanyNuclear Emergency

Generators

Alstom France

Propulsion Control

Systems

Alstom Grid UK

HDVC Power

Systems

Airbus Helicopters

EC130 Helicopter

EN-50128ISO 26262

IEC 61508 IEC 62304IEC-60880

DO-178

73

Recall Cause Categories

Majority of Recall Reasons related to 820.30 Design

Controls

– Design and revision of that design during the lifetime of the

device

74

0%

15%

30%

45%

1996 2006 2016

System Test

Implementation

Requirements Analysis

System Design

Model-Based Design Impact

More time on design (Increase Safety)

Less time on implementation and test

Percent Time Spent in Development Stages

Source: Arthur D. Little GmbH

- Studie "Kosten-/Nutzenanalyse der modellbasierten Softwareentwicklung im Automobil“, January 2007.

[chart translated]

projected

75

FDA & Model-Based Design

76

FDA-MathWorks MBD WorkshopExperiential Learning Program

2-Day Workshop conducted in Natick at MathWorks

10 attendees from CDRH, Ventilator and Anesthesia

division

Collaboration with Dräger Medical

– Presentation of closed-loop controller used in Dräger Devices

– Demo of Ventilator and Anesthesia machines

78

医療機器開発向けモデルベースデザインによる開発フローのご紹介会場 : E

時間 : 15:20-16:10

講演者 : MathWorks Japan

大塚慶太郎

関連セッションのご紹介

要求仕様書からモデリング、検証、コード生成まで、モデルベースデザインによる開発フローの一連の流れをご紹介いたします。

“実行可能な仕様書”

80

医療・介護機器の制御システム設計～モデルベースデザインにトライ～

コントローラ

制御異常

停止

PID

PID

PID

アクチュエータセンサプラント

制御電気機械

＋

－

人間

指令値制御量

概要：ロボットアームのプラントモデリング、コントローラ設計講演者：福井慶一セッション番号： E4

会場： E

時間： 16:20 – 17:10