Predictors of posture and shape (WP2) Objectives and vision

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1 Predictors of posture and shape (WP2) Objectives and vision Project funded by the European Union Seventh Framework Programme ([FP7/ ]) under grant agreement n (Collaborative project PIPER) PIPER Workshop 08/09/2015

2 Predictors of posture and shape 8:45 0:30 9:15 Welcome and registration 9:15 0:10 9:25 Workshop Introduction & objectives (Philippe Beillas, Université Lyon-Ifsttar) 9:25 0:10 9:35 Introduction to the PIPER project (Philippe Beillas) Specifications of position & personalizing tools, 9:35 0:40 10:15 applications and child modelling (PIPER WP1, Moderator: Norbert Praxl, PDB) Introduction (10 ) 10:15 0:45 11:00 Predictors of posture and shape (PIPER WP2, Moderator: Xuguang Wang, Université Lyon-Ifsttar) Objectives and vision 11:00 0:20 11:20 Coffee Break Positioning and personalization methods and tools 11:20 1:15 12:35 (PIPER WP3, Moderator: Erwan Jolivet, CEESAR) Data needs & Demonstrations of the development version of the segmentation (10 ) 12:35 0:30 13:05 PIPER tools (Moderator: Erwan Jolivet, CEESAR) 13:05 0:55 14:00 Lunch (with interactive demos in parallel) First database (10 ) Project roadmap, dissemination and licensing 14:00 0:25 14:25 (PIPER WP4, Moderator: Philippe Petit, LAB) Open discussion on PIPER roadmap Upcoming work (10 ) 14:25 0:30 14:55 (Moderator: Philippe Beillas) Personalizing 14:55 0:20 15:15 Coffee Break Positioning Generating and Validating Subject-Specific Human 15:15 0:35 15:50 Body Models (Matt Reed, UMTRI) Questions (5 ) ViVA - Virtual Vehicle Safety Assessment: Open 15:50 0:45 16:35 Source Digital Human Body Models and Crash Testing (Astrid Linder, VTI, Jonas Östh, Chalmers) 16:35 0:25 17:00 Topic: Open Source and Open Science (Jérôme Velut, Kitware) 17:00 0:30 17:30 Open discussion and concluding remarks 17:30 0:00 17:30 Adjourn PIPER Workshop 08/09/2015 WP2 Introduction Presenter : Xuguang Wang (UCBL)

detailed targets using a priori knowledge WP3 tools Personalized FE HB model in a

3 WP2 objectives Inputs User defined target subject (Gender, Age, Stature, BMI, ) Desired position (Driving, Walking, ) Human Body Model (GHBMC, TUMS, ) WP2 More detailed targets using a priori knowledge WP3 tools Personalized FE HB model in a desired position PIPER Workshop 08/09/2015 WP2 Introduction Presenter : Xuguang Wang (UCBL)

4 Introduction of a priori HBMS Child GHBMC THUMSV3 ViVA User defined Metadata & parsing rules Anatomical components Rigids Joints Import Specifications Guidelines GUI Module selection Visualization Quality metrics Interaction Input Target definition Dimensions Transformation modules Kriging Lightweight simulation PIPER Workshop 08/09/2015 WP2 Introduction Presenter : Xuguang Wang (UCBL) Export (model update) Personalized and positioned HBM FE Positions Contour based approach Tool to predict dimension, shape posture (a priori knowledge) Age Percentile Dimension Angle PIPER database Dimensions Regressions Shapes Open Source Modular framework Applications (child and adult models) 4

ANSUR 3D surface scans CEASAR (2500 USA, 2500 EU, 18 65 yrs, 1998-2000) IFTH 2006 (11562 French, 5-70 yrs, 2006)

5 A priori knowledge Demographic data e.g. NHANES Anthropometric data 1D, e.g. ANSUR 3D surface scans CEASAR (2500 USA, 2500 EU, yrs, ) IFTH 2006 (11562 French, 5-70 yrs, 2006) SizeGermany (13362 Germans, 6-87 yrs, 2007/2008) UMTRI children ( yrs) Statistical anthropometric models Hierarchical estimation method from ANSUR data (You and Ryu, 2005) Combining demograhic and anthropometric data (Parkinson and Reed, 2010) Statistical body shape models (Shu et al from CEASAR data, Park and Reed, 2015 for children) But few 3D scan data are publicly available Shu, 2012 PIPER Workshop 08/09/2015 WP2 Introduction Presenter : Xuguang Wang (UCBL) UMTRI Child body shape modeling

publicly available 50 specimen from ULB (Verel, 2015) PIPER Workshop

6 A priori knowledge Huge but sparse anatomical/clinical data Skeleton by body part Internal organs A few SSMs reported for isolated body parts but not publicly available 50 specimen from ULB (Verel, 2015) PIPER Workshop 08/09/2015 WP2 Introduction Presenter : Xuguang Wang (UCBL) 5 organes from Zhou et al., 2014

A priori knowledge Inter/ext relationships

Reed et al, 1999, Peng et al, 2015) Internal

measurements (HUMOS, 64 heathy adults Bertrand

and external measures UCBL upright MRI Data

, 2015 Bertrand s Landmark data (Humos) license

7 A priori knowledge Inter/ext relationships Joint centers from external Als (e.g. Reed et al, 1999, Peng et al, 2015) Internal and external dimensions from few external measurements (HUMOS, 64 heathy adults Bertrand et al, 2009) But few data with both internal and external measures UCBL upright MRI Data (n=9) Nérot et al., 2015 Bertrand s Landmark data (Humos) license being discussed with ENSAM PIPER Workshop 08/09/2015 WP2 Introduction Presenter : Xuguang Wang (UCBL)

8 A priori knowledge Functional data Joint ROMs (e.g. Doriot and Wang, 2006 for joints of upper body, Wang 1998 for shoulder axial rotation) Joint kinematic constraints (e.g. Sholukha et al for knee joint axes coupling) Task specific position Driving posture (e.g. Reed et al, 2000, Peng, 2015) Wang (1998) Peng et al (2015) PIPER Workshop 08/09/2015

9 Specific PIPER data Full body (head to toe) PMHS CT-scans CEESAR/LAB ~100; Univ Lyon-Ifsttar ~18 scans Segmentation in progress SSMs for isolated body parts and relationships between them PIPER Workshop 08/09/2015 WP2 Introduction Presenter : Xuguang Wang (UCBL)

WP2 vision Database (litterature + PIPER specific) Anthropometric and geometric modelling toolbox 1D Anthrop model 3D Ext body shape a given posture Inc landmarks Int.

10 WP2 vision Database (litterature + PIPER specific) Anthropometric and geometric modelling toolbox 1D Anthrop model 3D Ext body shape a given posture Inc landmarks Int. skeleton shape Inc joint properties & landmarks Int. organe shape for a position Material properties Consistency checking and correction PIPER HB templates Whole body model for a given posture (e.g. Standing) Positioning tool of internal skeleton simplified as RB linkage Targets for WP3 tools PIPER Workshop 08/09/

11 Segmentation Project funded by the European Union Seventh Framework Programme ([FP7/ ]) under grant agreement n (Collaborative project PIPER) PIPER Workshop 08/09/2015

Image based: Data Processing segmentation

12 Image based: Data Processing segmentation workflow Reference model CT scans Preparation Filtering & Segmentation Segm. (Mesh) Registration/ Correspondence Models for PCA Stat. work Separated sessions PIPER Workshop 08/09/2015 WP2: Segmentation Presenter: Philippe Beillas 12

Automated in custom programs (ITK based, will be released Apache 2.

13 Preparation Problem: CT-scans in multiple sessions Bodies are frozen rigid registration pipeline Merge dicoms into nii Rigid registration (translation+rotation) Blend sessions Automated in custom programs (ITK based, will be released Apache 2.0 license) Semi-automatic box removal Registration and merging PIPER Workshop 08/09/2015 WP2: Segmentation Presenter: Philippe Beillas

14 Manual/semi automated Trials: combinations of filtering (median, DoG, thresholding, mask editing) Volmo: custom programs SOTON: Simpleware Univ Lyon-Ifsttar: Slicer, Meshlab Results: time consuming OK for objects that can be easily separated (some bones ) A lot of manual editing for some other problematic for spine 4 full bodies processed (SOTON, VOLMO) used for reference models (template) after cleaning segmentation Volmo SOTON PIPER Workshop 08/09/2015 WP2: Segmentation Presenter: Philippe Beillas

Image to image registration Trial: elastix (Univ Lyon-Ifsttar) Define reference mask: voxelize a reference model; Deform the mask onto the target (thresholded) using Elastix Bspline deformation,

15 Image to image registration Trial: elastix (Univ Lyon-Ifsttar) Define reference mask: voxelize a reference model; Deform the mask onto the target (thresholded) using Elastix Bspline deformation, optimization process Labels are still separated Can be meshed (or transformation applied to mesh) Seem to provide approximation (not as local as manual segmentation but ok for dimensions measurements? Mask labels=bones Deformed mask Red, Green: mask & target Orange: overlay of mask & target PIPER Workshop 08/09/2015 WP2: Segmentation Presenter: Philippe Beillas

16 Processing workflow Image based: Reference model CT scans Preparation Filtering & Segmentation Segm. (Mesh) Registration/ Correspondence Model for PCA Stat. work Mesh based: Reference model CT scans Preparation Filtering Model to image Registration Model for PCA Stat. work INRIA-LAB-CEESAR (not strictly par of PIPER) PIPER Workshop 08/09/2015 WP2: Segmentation Presenter: Baptiste Moreau (LAB) 16

17 Model to image registration Aim: To deform a reference model until it matchs the target anatomy Reference CT-scan + Reference model Target CT-scan Subject-specific geometric mesh PIPER Workshop 08/09/2015 WP2: Segmentation Presenter: Baptiste Moreau (LAB) 17

18 PIPER Workshop 08/09/

19 Model to image registration Mechanical behavior on the reference model An Open Source framework primarily targeted at real-time simulation, with an emphasis on medical simulation ICP* forces To deform the reference model Manual interactions To lead the deformations *ICP: Iterative Closest Point PIPER Workshop 08/09/2015 WP2: Segmentation Presenter: Baptiste Moreau (LAB) 19

Model to image registration Accuracy of segmentation* (on a femur: Dice=0,97) Correspondence between points from one subject to the other: good for the next phase (PCA) 26 PMHS (55-91 y.

20 Model to image registration Accuracy of segmentation* (on a femur: Dice=0,97) Correspondence between points from one subject to the other: good for the next phase (PCA) 26 PMHS (55-91 y.o) done for now Duration for 1 subject: LowerLimb ( 15min); TAP ( 30min) Age *EvaluateSegmentation: PIPER Workshop 08/09/2015 WP2: Segmentation Presenter: Baptiste Moreau (LAB) 20

21 Conclusion Processing method about to be finalized: Alternative to manual segmentation needed to increase sample number interesting tracks Possibility Merge sessions (and filter) (mesh initiation by nonlinear registration) Mesh to image registration (INRIA tool) Validation PCA Process may not be as local as manual segmentation but may be sufficient for the intended purpose? Documented Process (public deliverable) to facilitate data additions PIPER Workshop 08/09/2015 WP2: Segmentation Presenter: Baptiste Moreau (LAB) 21

22 First database Project funded by the European Union Seventh Framework Programme ([FP7/ ]) under grant agreement n (Collaborative project PIPER) PIPER Workshop 08/09/2015

23 First PIPER database Objectives: Store shape, posture, and predictor information to support the personalizing and positioning tools Constraints: 1.Support data from different sources Different nomenclature for the same data Different types and format of information Landmarks, meshes, measurements Different quality Completeness (e.g, partial skeleton vs full skeleton) 2. Easily accessible from the WP2 tools Statistics / data for statistics in appropriate format Also, easy generation of WP2 output 3.Upgradable Adding data (simple, robust, welcoming) Updating the structure/format of the database 4.Public access Licencing Universal format, free of software (and WP) dependency Permanency, can be retrieved and used by others PIPER Workshop 08/09/2015 WP2: First PIPER database Presenter: Christophe Lecomte

24 Database content (type) Type of data Shapes Landmarks 10 pelvis shape landmarks Measurements Meshes Predictors Statistics Posture Lopez-Costas et al., 2008 (Fig.9 and 1) PIPER Workshop 08/09/2015 WP2: First PIPER database Presenter: Christophe Lecomte 24

25 Database structure Structure of the PIPER database Piper Database listdatasetsfiles.txt Dataset SotonCTscans -Data item SCT1 -Data item SCT2 - -Data item SCT78 -DataReferenceSCT Dataset DatasetName -Data item DSN1 -Data item DSN2 - -Data item DSN15 -DataReferenceDSN - DataReference Landmark codes Tools Loading datasets Checking data consistency, Data is organised in datasets composed of data items. Information about the data (items) summarised in inventory files origin of the data, its nature, and its format anonymous information about the subjects CT scan or MR images where generated, how landmarks were measured and how surfaces of body parts were segmented. Common information between the datasets in a specific database level directory DataReference. reference codes for describing the names of body parts and landmarks Instructions for adding new datasets, data items, and updating the structure of the database Additional tools for loading the data, etc. Uses a flat, file based approach (for simplicity, permanency, etc.). PIPER Workshop 08/09/2015 WP2: First PIPER database Presenter: Christophe Lecomte 25

Database current content Shared on the project

Instructions LTE635 dataset: Dataset inventory file

cartesian coordinates One STL file per surface mesh

26 Database current content Shared on the project repository Common: Reference Codes Database Instructions LTE635 dataset: Dataset inventory file Description of landmark content Landmark data in cartesian coordinates One STL file per surface mesh PIPER Workshop 08/09/2015 WP2: First PIPER database Presenter: Christophe Lecomte 26

27 Dataset structure (current) Example of dataset fields/values (1 item) Dataset Subject Source image Data PIPER Workshop 08/09/2015 WP2: First PIPER database Presenter: Christophe Lecomte 27

Current datasets Datasets(/data available) include: CCTs.

manifold surface meshes of individual bones, external skin, and costal

van Sint Jan) Additional landmarks for joint identification

Two datasets of manually segmented PMHS subjects (male/female VOLMO

through semi-automatic segmentation (LAB/CEESAR/INRIA) of PHMS CT

28 Current datasets Datasets(/data available) include: CCTs. Two datasets of manually segmented PMHS subjects (male/female): manifold surface meshes of individual bones, external skin, and costal cartilage (full body) Up to 484 skeletal landmarks (ref. van Sint Jan) Additional landmarks for joint identification Repositioned skeleton to use as WP2 reference model CCTv. Two datasets of manually segmented PMHS subjects (male/female VOLMO from CT scans) Covers full body Used as target model in the LAB/CEESAR/INRIA approach CCTc. A series of twenty five datasets of skeletal surface meshes obtained through semi-automatic segmentation (LAB/CEESAR/INRIA) of PHMS CT scans Manifold surfaces of lower limb and thorax bones CCTc, ex3 CCTc, ex1 CCTc, ex2 F, CCTs635 PIPER Workshop 08/09/2015 WP2: First PIPER database Presenter: Christophe Lecomte 28

Current datasets (ctd ) Datasets(/data available) include: LBMC.

MRI, using non-linear model deformation: manifold surface meshes of

abdomen Four postures (seated, standing, forward flexed as a cyclist, and

Head surfaces of children and adult (and partially segmented full 1 Yr

6 year old, and one male (45 year old) PCT.

scans: A number of corresponding tibias also available F, 1Yr M05 M01 F02

29 Current datasets (ctd ) Datasets(/data available) include: LBMC. Meshes and landmarks for 9 live subjects (6 males/3 females) from low-res MRI, using non-linear model deformation: manifold surface meshes of individual organs, bones, body parts, body cavities Covers thorax and abdomen Four postures (seated, standing, forward flexed as a cyclist, and supine for reference) KTHh. Head surfaces of children and adult (and partially segmented full 1 Yr skel.): skull, gray matter, white matter, head skin two female aged 1 and 6 year old, and one male (45 year old) PCT. A dataset of 120 manually segmented femurs from 84 adult subjects from CT scans: A number of corresponding tibias also available F, 1Yr M05 M01 F02 Femurs F, 1Yr M, 45Yr F, 6Yr F01 Kidney R Standing Supine ForwardFlex F03 Liver White matter PIPER Workshop 08/09/2015 WP2: First PIPER database Presenter: Christophe Lecomte 29

30 External datasets Datasets(/data available) include: KLL. A dataset of raw landmark data from the Robert J. Terry Anatomical Collection used in Kepple et al lower limb landmarks (different types: anatomical, muscle attachment, etc.) for 52 subjects, divided in male/female and black/white categories. Public domain (kindly provided by Tom Kepple) Small sample of landmarks (about 10 from femur) CAESAR. Partial information from the CAESAR measurement data. Extended set of external measurements Predictor information (stature, BMI, etc.) available KLL will be part of the PIPER database (public domain). CAESAR won t (for licensing reasons). A user with appropriate licence would be free to add the data in their local database. PIPER Workshop 08/09/2015 WP2: First PIPER database Presenter: Christophe Lecomte 30

31 Tools Interfaces and tools Tools are provided as an add-on to the database. Matlab scripts are available to load the database (save in binary mat format) Matlab/Octave (Open source alternative to Matlab) scripts to check data consistency (manifold, normals) Interfaces Interface to select predictors, landmarks of interest, etc. would be useful. Interface to WP3 to exchange information about WP2 outputs scripts could be packaged so that the data is available octave/matlab/r (not part of PIPER) PIPER Workshop 08/09/2015 WP2: First PIPER database Presenter: Christophe Lecomte 31

32 Future database upgrades Future additions: PIPER segmentations (CEESAR CCT series, etc.) Public domain datasets as ANSUR, NHANES III, Bertrand 2009 landmarks (if agreement), etc Data from literature (regressions, etc.) Open to external contributions (public domain or compatible with PIPER database licence) Environment Versioning Retain versions of all reference codes, data items,... Quality assessment/measure + Editing Relationship between data items (parents, child, etc.) Also, XML format (besides the flat text files), storage of statistical data, etc. PIPER Workshop 08/09/2015 WP2: First PIPER database Presenter: Christophe Lecomte 32

33 Conclusion The structure of the database seems flexible enough for future needs within the project adding datasets, upgrading its format, supporting different types of predictor, shape and posture information. Some rigour is necessary in the formatting and conventions. Formalised editing procedures would be very useful for the addition of new data and quality assessment of existing data. PIPER Workshop 08/09/2015 WP2: First PIPER database Presenter: Christophe Lecomte 33

34 Vision and Upcoming work Project funded by the European Union Seventh Framework Programme ([FP7/ ]) under grant agreement n (Collaborative project PIPER) PIPER Workshop 08/09/2015

35 Objective: Vision and strategy: personalizing Tools to provide statistical shape and dimension information based on diverse available literature and data that can be used by WP3 for personalised input (for predictors as gender, age, BMI or for a specific subject) Strategy: Build up and combine component tools Progressive developments Pragmatic choices Hierarchical organisation Track result confidence Data quality Statistical model error Landmarks and dimensions to surface models PIPER Workshop 08/09/2015 WP2: Vision and upcoming work Presenter: Christophe Lecomte (SOTON) 35

Reference model Reference models in sitting and standing positions For definition of landmarks, measurements, etc For matching landmarks, measurements As a (surface) support for input

spline curve Individual vertebrae & ribs About 500 landmarks Manually segmented PMHS Skin to be deformed from PMHS posture Deformation if single shape Matching if SSM available Spare

36 Reference model Reference models in sitting and standing positions For definition of landmarks, measurements, etc For matching landmarks, measurements As a (surface) support for input to WP3 Mean pelvis shape (from Kepple et al data) PIPER Templates male/female (Standing/sitting) Articulated rigid bones ISB recommended joint coordinates Spine following user defined spline curve Individual vertebrae & ribs About 500 landmarks Manually segmented PMHS Skin to be deformed from PMHS posture Deformation if single shape Matching if SSM available Spare definition of landmarks on WP3 FEM Combine different info on single Human Model meshes PIPER Workshop 08/09/2015 WP2: Vision and upcoming work Presenter: Christophe Lecomte (SOTON) 36

37 Shapes Landmarks or surface meshes Alignment/GPA (Procrustes) for individual body parts Correlation within and between the body part sets Reassembly (joint location) and articulation of the mean or samples from the Statistical Shape Models Body part #1 Body part #2 Assembly (both mean & covariance) Raw data (landmarks/aligned meshes) Shapes (nuisance as rotation, removed) Statistical Shape Model SSM (mean/covariance) PIPER Workshop 08/09/2015 WP2: Vision and upcoming work Presenter: Christophe Lecomte (SOTON) 37

Predictor to shape If enough data is available

), one can derive multiple regression models

variables Statistical error Different populations

(Parkinson Reed approach) Might be able to build

38 Predictor to shape If enough data is available (ANSUR, etc.), one can derive multiple regression models Multiple predictors Modes of data Physical variables Statistical error Different populations by upsampling of virtual pop. (Parkinson Reed approach) Might be able to build such regression models for the PCA components of the variance of the SSM models. Effects on landmarks versus effects on shape and shape components? PIPER Workshop 08/09/2015 WP2: Vision and upcoming work Presenter: Christophe Lecomte (SOTON) 38

Positioning: general approach Personalized shape model in a predefined posture (Sitting/Standing) Userdefined/PIPER simplified articulated

39 Positioning: general approach Personalized shape model in a predefined posture (Sitting/Standing) Userdefined/PIPER simplified articulated skeleton Forward and Inverse kinematics + functional & task related knowledge Targets for WP3 e.g. RAMSIS e.g. RPx 39 PIPER Workshop 08/09/2015

Positioning: upcoming work Definition of kinematic linkage model Userdefined/PIPER simplified articulated skeleton Forward and Inverse kinematics +

40 Positioning: upcoming work Definition of kinematic linkage model Userdefined/PIPER simplified articulated skeleton Forward and Inverse kinematics + functional & task related knowledge Implementation of inverse kinematic solver Case of the spine e.g. RPx Targets for WP3 40 PIPER Workshop 08/09/2015

sitting posture Spinal postural variations in positioning Using

41 Upcoming work for positionning Spine positioning = define? (Robbins 1983; Black 1996; Reed 1999) Most probable spinal sitting posture Spinal postural variations in positioning Using a priori biomechanical information (Monnier 2007) PIPER Workshop 08/09/

42 Upcoming work for positionning Spine positioning : Physiological spinal curvature biomechanical information (D2.1. extensive review) - Age - Gender - Morphotypes = f ( ) (Roussouly 2005, 2011) (Burdi 1969; Kasai 1996; Boyle 2002) PIPER Workshop 08/09/2015 (Klinich 2004, 2012) 42

extensive review) Physiological spinal curvature = f (posture) (Beillas

43 Upcoming work for positionning Spine positioning : biomechanical information (D2.1. extensive review) Physiological spinal curvature = f (posture) (Beillas 2009) (Lord 1997; Chabert 1998; Wagnac 2011) Mostly based on standing to sitting relationships (Black 1996; Harrison 2000) PIPER Workshop 08/09/

44 Upcoming work for positionning Spine positioning : implementation 1) Direct kinematics (no constraints = unique solution) Coordination laws, e.g.: + Physiological distribution + Physiological kinematic couplings (e.g. Case of more complex motions) PIPER Workshop 08/09/

Upcoming work for positionning Spine positioning : implementation 2) Controlled b-splines (based on few targets) Bezier curves (Hobby 1986), based on Will Robertson (2013):

45 Upcoming work for positionning Spine positioning : implementation 2) Controlled b-splines (based on few targets) Bezier curves (Hobby 1986), based on Will Robertson (2013): Natural b-spline Playing with slope and tension Matching (Robbins 1983) Standing Sitting relationships? Other postural relationships? PIPER Workshop 08/09/

46 Upcoming work for positionning Spine positioning : implementation 3) Inverse kinematics (multiple constraints = multiple solutions) e.g. Man3D/ManStar/RPx 7 joints C0/C1 C7/T1 T4/T5 T8-T9 T12/L1 L3/L4 L5/S1 (Monnier 2007) (Simonidis 2007) Requires optimisation RPx: Further spinal levels will be added Improved coordination / distribution laws (kinematics/stifness) will be tested PIPER Workshop 08/09/

47 Upcoming work for positionning Spine positioning : deliverables Two levels: - Global parameters (e.g. angles) that may act as constraints to WP3 and as feedback to the user for the positioning (WP1) No model compatibility issues - Target landmarks for WP3 (e.g. based on ISB joints / landmarks definitions) Models (rigid/fe) compatibility issues will need to be solved at WP3 level PIPER Workshop 08/09/

48 Upcoming work for positioning Definition of kinematic linkage User defined / PIPER Spine coordination Implementation of an Inverse kinematic solver taking into account constraint-priority PIPER Workshop 08/09/2015 WP2: Vision and upcoming work Presenter: Xuguang Wang (UCBL) 48

49 Discussion & question Summary for WP2 But A toolbox for anthropometric, geometric and kinematic modeling A segmentation pipeline An open source database More data are needed: ext body shape, int/ext relationships Methods for combining heterogeneous data are needed Compatibility issues between simplified articulated rigid bodies and deformable FE models need to be solved PIPER Workshop 08/09/

Development of an Open Source Framework to position and personalize Human Body Models

NHTSA Workshop Nov 6, 2016 Development of an Open Source Framework to position and personalize Human Body Models P. Petit (LAB Peugeot Renault, FR) P. Beillas (Univ. Lyon 1-Ifsttar, FR) S. Kirscht (TU