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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