from motion capture to muscular activity in lower

As muscles contract, volt level electrical signals are created within the muscle that may be ... Run then Close. 27 ... Can cancel out “real” signal. ▫ Kalman filter/ ...
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TUTORIAL

MUSCULOSKELETAL SIMULATION : FROM MOTION CAPTURE TO MUSCULAR ACTIVITY IN LOWER LIMB MODELS

Nicolas Pronost and Anders Sandholm

Musculoskeletal simulation ?  What is it ?

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Musculoskeletal simulation ?  What is it ?

Henry Gray, Anatomy of the human body, 1918

 Musculo

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Musculoskeletal simulation ?  What is it ?

Sylvia S. Blemker, Stanford University, 2006

 Musculo

Human anatomy MUSCULOSKELETAL SIMULATION

Musculoskeletal representation

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Musculoskeletal simulation ?  What is it ?

OpenSim, University of Stanford

 Musculo

Human anatomy

Musculoskeletal representation

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Action lines

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Musculoskeletal simulation ?  What is it ?  Musculo  Skeletal

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Musculoskeletal simulation ?  What is it ?  Musculo  Skeletal

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Musculoskeletal simulation ? pelvis

 What is it ?  Musculo  Skeletal

l_hip

r_hip

vl2

l_knee

r_knee

vl3

l_ankle

r_ankle

vt4

l_subtalar

r_subtalar

l_mid_foot

r_mid_foot

l_toe

r_toe

Segments connected by joints and hierarchically organized

vt5 r_clav

vt6

r_hand

head_top l_hand

Rigid bodies with mass, inertia matrix and CoM

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Musculoskeletal simulation ?  What is it ?  Musculo  Skeletal  Simulation

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Musculoskeletal simulation ?  What is it ?  Musculo  Skeletal  Simulation means analysis

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Musculoskeletal simulation ?  What for ?

3DAH Marie Curie Project

OpenSim, University of Stanford

 Analyze athletic performance

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Musculoskeletal simulation ?  What for ?

AnyBody Technology, Aalborg University

 Analyze athletic performance  Design ergonomically safe environments

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Musculoskeletal simulation ?  What for ?

3DAH Marie Curie Project

OpenSim, University of Stanford

 Analyze athletic performance  Design ergonomically safe environments  Understand and/or treat movement disorders

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Musculoskeletal simulation ?  What you do with ?    

Visualize complex movement patterns Test “what if” scenario Estimate data difficult to measure Identify cause-effect relationships

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Outlines of the tutorial  Objective : To perform a musculoskeletal simulation from A to … V     

Acquisition of the data Definition of the model Inverse Kinematics solving Muscular activation estimation Validation of the simulation

 Extra features    

How to create a model ? Interactions with medical imaging Towards more visualizations Simulating tendon transfer surgery MUSCULOSKELETAL SIMULATION

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Context  Tools  OpenSim     

Open-source musculoskeletal simulation platform Based on SimTK (biological dynamics) Performs SCALE, IK, ID, RRA, CMC and FD Provided with validated musculoskeletal models GUI and command line based

 Subject specific data  Motion capture (crouch) with ground reaction forces  EMG signals

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STEP 1 : ACQUISITION OF THE DATA

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Acquisition of the data

(1/3)

 Motion capture

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C. Nester, University of Salford, 2007

QUALYSIS

QUALYSIS

VICON

QUALYSIS

 3D position of anatomical landmarks over time  Skin markers vs. clusters vs. bone pins

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Acquisition of the data

(1/3)

 Motion capture

3DAH Marie Curie Project

 3D position of anatomical landmarks over time  Skin markers vs. clusters vs. bone pins

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Acquisition of the data

(2/3)

 Ground reaction forces

AMTI

 6D (force + moment) kinetics reaction of the body  To solve the inverse dynamics analysis (through the Newton’s laws of motion)

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Acquisition of the data

(3/3)

 Electromyography (EMG) signals

NORAXON

3DAH Marie Curie Project

 As muscles contract, volt level electrical signals are created within the muscle that may be measured from the surface of the body

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MUSCULOSKELETAL SIMULATION

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STEP 2 : DEFINING A MODEL

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Loading a model    

Start OpenSim Menu FILE >> Open Model… Select /TutorialData/GenericModel.osim Manipulate Menu bar, 3D view, Coordinates and Navigator panels

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Scaling the model – Step 1  Scale factors are applied from ratios between markers distances in model and in mocap

Original model

Standing pose

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Scaled model

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Scaling the model – Step 2  The virtual markers are moved to match the positions of the experimental markers

Scaled model

Standing pose

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Subject model

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Scaling the model    

Menu Tools >> Scale Model… Settings >> Load Settings… Select /TutorialData/Setup_SCALE.xml Run then Close

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STEP 3 : INVERSE KINEMATICS

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Inverse Kinematics  Goal : to find the joint angles of the model that best reproduce the experimental kinematics of the subject’s motion  Weighted least squares optimization solver with the goal of minimizing marker errors

 q = joint angles , xiexp = experimental position of marker i xi(q) = virtual position of marker i

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Inverse Kinematics    

Menu Tools >> Inverse Kinematics… Settings >> Load Settings… Select /TutorialData/Setup_IK.xml Run then Close

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STEP 4 : MUSCULAR ACTIVATION ESTIMATION

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Muscular activation estimation  Residual Reduction Algorithm (RRA)

OpenSim, University of Stanford

 Dynamics inconsistency due to errors in kinematics and kinetics measurements and in rigid body modeling  Additional “residual” forces and moments are added F + Fresidual = m . a  Modification of the kinematics and the CoM to reduce Fresidual without significantly altering the simulation

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OpenSim, University of Stanford

Effect of reducing residuals

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Muscular activation estimation  Computed Muscle Control (CMC)  To compute a set of muscle excitations tracking the desired kinematics PD control law defines the desired accelerations Static optimization distributes the loads across actuators Forward dynamics conducts the simulation advancing in time Repeated until time is advanced to dt OpenSim, University of Stanford

   

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Muscular activation estimation    

Menu Tools >> Computed Muscle Control… Settings >> Load Settings… Select /TutorialData/Setup_RRA.xml Run then Close

   

Menu Tools >> Computed Muscle Control… Settings >> Load Settings… Select /TutorialData/Setup_CMC.xml Run then Close

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STEP 5 : VALIDATION OF THE SIMULATION

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Validation of the simulation  Comparison against experimental data : EMG

right vastus medialis crouch motion

right soleus crouch motion

muscle activation from simulation MUSCULOSKELETAL SIMULATION

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raw EMG

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Post processing of EMG  Electrical potential generated by muscle cells  Measured in volt, about 90mV  Signal need to be post–processed  Noise  Cross reading from other muscles  Rectified

 Filtering  Box filtering 

Can cancel out “real” signal

 Kalman filter/smoother 

More computational intense

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Post processing of EMG

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Simulation vs. EMG

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HOW TO CREATE A MODEL ?

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How to create a model ?  We need  Palpable bony landmarks  3D position (from mocap), definition of a coordinate system

 Body parts  Moment of inertia, mass, position of center of mass

 The joints  DoF, axis and center of rotation

 Muscle and ligament attachment sites  Origin and insertion (and via points) positions, fiber and tendon lengths, mass, pennation angle…

 Bony constraints  Warping points and bony contours

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Example  Klein Horsman dataset  University of Twente, The Netherland  [Klein Horsman, Koopman, Van der Helm, Poliacu Prosé, Veeger. Morphological muscle and joint parameters for musculoskeletal modelling of the lower limb, Clinical Biomechanics (22), pp 239-247, 2007]

 Measurements performed on a right lower extremity of a male cadaver (age 77, height 1.74m, weight 105kg)

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Datasets

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Datasets  21 markers  4 body parts  pelvis, femur, tibia, foot

 58 muscles from 163 action lines  5 joints  hip, knee, femur-patella, ankle subtalar

 2 wrapping constraints  Gastrocnemius around femur condyle  Iliopsoas around the pelvis

 104 via points MUSCULOSKELETAL SIMULATION

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Validation of the model  For musculoskeletal simulation use  Technical part of formatting the data  Compare simulation results with  same motion and previous models  experimental data (EMG)

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INTERACTION WITH MEDICAL IMAGING

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Interaction with medical imaging  Benefit from the intensive use of medical images to create and validate models DT-MRI + fiber tracking

High resolution of joints

Cross sectional long-leg

Dynamic MRI MUSCULOSKELETAL SIMULATION

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Interaction with medical imaging  Benefit from the intensive use of medical images to create and validate models and simulations DT-MRI + fiber tracking

Fiber directions in model

High resolution of joints

Attachment points and FE simulations

Cross sectional long-leg

Attachment points and scaling validation

Dynamic MRI

validation of kinematics

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Interaction with medical imaging  MRI viewer in OpenSim  Alignment using common markers  Comparisons between tendon areas and action lines extremities

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TOWARDS SCIENTIFIC VISUALIZATIONS

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Towards more visualizations  To help estimating results and tuning settings  Scale  Variation in factors, displacements in second inner step

 IK  Error over time or time-independent

 CMC  Magnitude of activation, reserve or residual forces

 Validation  Difference between activation and EMG patterns

 To integrate external results  Nodal displacements or pressure from FE simulations

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Towards more visualizations

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Thank you for your attention  References    

3DAH Marie Curie Project EPFL – VRLAB Aalborg University – SMI OpenSim

[email protected]

MUSCULOSKELETAL SIMULATION

http://3dah.miralab.unige.ch http://vrlab.epfl.ch http://www.smi.hst.aau.dk https://simtk.org/home/opensim

[email protected]

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