Musculoskeletal modelling:EMG-Driven models

GuillaumeRaoAix-Marseille Université, Marseille, France

Institut des Sciences du Mouvement, UMR 7287, Marseille, France

Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System

From brain tomuscleactivation

Meaningful information

EMG-Driven models

• Philosophy:Mechanically-basedobjectivefunctionsfailed ingenerating adequatemuscular activationsforalargevariety oftasks and/orpopulation

• Two directions:– Bounding thesolutionspace using EMGdata– Using EMGdataasinputtoestimate themuscular activations

Bounding thesolutionspace

• Therecorded EMGis used asconstraint oftheoptimizationprocedure

• standforthemuscletensionswith additionalconstraints:

Vigourouxetal.,2007

𝑡" 𝑜𝑟𝑡&

𝑤𝑖𝑡ℎ0 ≤ 𝜇 ≤ 0.05

Bounding thesolutionspace

• EMG-constrained muscleforcesarecloser toexperimentalactivations,particularly forantagonist muscles

• Efficientprocedure,butlimited toquasi-isometric contractions

Vigourouxetal.,2007

• Systemdynamics– j =1:mmuscles– k =1:p DoFs– l =1:r jointreactions

Basic principles

Activationdynamics

uj aj fjMusculo-tendon

(contraction)dynamics

Skeletaldynamics

gl

Muscleexcitation

Muscleactivation

Musculo-tendonforces

Ligamentandcontactforces

Kinematicsparameters

𝑞, �̇�, �̈�

EMGdata

Courtesy ofR.Dumas

EMGtoactivationProcessing

• Fromraw datatoactivation:4steps

– Rectification

– Low-pass filtering:Butterworth zero timelag,cut-off freq ≈ 5𝐻𝑧

– Activationdynamics step

– Non-linearization step

7

Activationdynamics

• Generalexpressionofarecursive filter torepresent theinfluenceofprevious « activationstates »onthecurrentactivation,u(t)

• d=Electromechanical delay• 𝛽7𝑎𝑛𝑑𝛽; represent theactivationdynamics coefficients

8

Non-linearization

• Forceis non-linearly related toactivation,even forisometrictasks

• Afactorstandsfortheshape factor

9

SEE

Muscle

Force

Muscle

ActivationActivationDynamics

ContractionDynamics

Input

EMG

nMuscles

Forward-InverseEMG-Driven model

Gérusetal.,2011,2012

a

Series Elastic Element(SEE)

TENDON- APONEUROSIS

Zajac (1989)

α: pennation angle

Force-length relationshipActive component Force-velocity relationship

Force-length relationshipPassive component

0 0.01 0.02 0.030

0.5

1

Non-linearregion Linearregion

Strain

Norm

alize

dForce

SEEε

TightenedFibersCrimpFibers

0 0,01 0,02 0,03

0,5

0.5 1 1.50

0.5

1

Normalized fiber length

Nor

mal

ized

forc

e

1050-5-100

0.5

1

1.4

Normalized fiber shortening velocity

Nor

mal

ized

forc

e

ConcentricEccentric

0.5 1 1.50

0.5

1

Normalized fiber length

Nor

mal

ized

forc

e

4

Hill-typemodel

SEE

Muscle

Force

Muscle

ActivationActivationDynamics

ContractionDynamics

Musculo-skeletalgeometry

Input

EMG

Mouvement

∑Muscle

Moment

MultijointDynamic

nMuscles

RecordedbytorquemeterOR

-210

-160

-110

-60

-10

MomentscomparisonAjdustementofparametersbyoptimization

SimulatedAnnealing (globalminimum)

5

Forward-InverseEMG-Driven model

ma

Gérusetal.,2011,2012

SEE

Muscle

Force

Muscle

ActivationActivationDynamics

ContractionDynamics

Musculo-skeletalgeometry

Input

EMG

Mouvement

∑Muscle

Moment

MultijointDynamic

nMuscles

RecordedbytorquemeterOR

5

EMG-Driven model

ma

-210

-160

-110

-60

-10Gérusetal.,2011,

2012

Parameters tooptimize

• Those related totheEMG-processing:

Symbol Variable Bounds Applied to

𝛽7 &𝛽; Filter coefficients -0.8<𝛽7 &𝛽; <0.95 Each muscle

d Electro-mechanical Delay 10ms<d<80ms Each muscle

A Shapefactor 0.01<A<0.1 Each muscle

Parameters tooptimize

• Those related totheHill-typemusclemodel

Variable Bounds Applied to

OptimalFiberLength OFL± 5% Each muscle

TendonSlackLength TSL± 5% Each muscle

Slope oftheOFL/activation

line0<𝜆 <0.25 Each muscle

Gainfactor 0.5<G<2 Each musclegroup

Why include EMGdata?

• EMGdatacollectionandprocessing is rather « boring »withlotsofexperimental issues(skinpreparation,electrodepositionings,crosstalk effects…)

• Datacollectionislimited tosuperficial muscles(thussometimes poorly representing therealpatternofactivations)

• Adding parameters inanoptimization procedure is never agoodidea

BUT!

Why include EMGdata?

Diabetic patientsandcontrolsubjectsmuscular activationsduring gait(Kwon etal.,2003)

Cansuch activationsbegenerated bythesame

energetically-based criterion?

Why include EMGdata?

• Tobe ascloseaspossibletotherealactivationsgenerated bythesubject (even unbalanced and/orunnatural ones)andhaveanincreased « trust »intheinputdata

• Tobe abletoestimate muscleforceseven fortasks whereclassical cost functions (energeticaly based forexample)arenotapplicable

• Toget individualized strategies tocope with pathology and/orimpairment (Shao andBuchanan,2008forstrokepatients)

Actual developments

• Howtoavoid numerous recordings andguidedeep musclesactivationscreation?

• Musclesynergies represent howmusclesareassembled asfunctional groupstoachieve agoal-directed task.

• Usually done using NonNegative MatrixFactorization (NNMF)

E=EMGmatrixW=muscleweightingsC=time-varying profiles

Musclesynergies

Clark&Ting,2010

Musclesynergies

Hug etal.,2011

Actual developments

Sartorietal.,2013

Synergiesasinputs

Other possibleideas tobound thesolutionspace

• EMG-EMGCoherence • Functional ConnectivityDynamics

Vernooij etal.,InrevisionCharissou etal.,2016

limits

• Emg-to-forcerelationship forpathology is notknown (Serge,Bohnes 2016forCP)BUT it’s less than likely that aCPkidmovesfollowing anenergetically-based criterion

->« EMG-helped »procedures areneeded

• Inputdata(tendonproperties, fibergeometry),activationdynamics,jointgeometry, objectivefunctions->Florent

Conclusion

• Uptonow,EMG-Driven models allows toinclude subject- ortask- specific activationdatatoestimate muscleforces

• Cumbersome process,butunequalled results

• Actual developments tendtosimplify theprocedure bytreating musclesas«goal-dependant functional groups »

• Fewdataavailable onneurologically impaired EMG-Forcerelationships

Musculoskeletal modelling:EMG-Driven models

GuillaumeRaoAix-Marseille Université, Marseille, France

Institut des Sciences du Mouvement, UMR 7287, Marseille, France

Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System