An Efficient Probabilistic Methodology for Incorporating Uncertainty in Body Segment Parameters and Anatomical Landmarks in Joint Loadings Estimated From Inverse Dynamics

2008 ◽  
Vol 130 (1) ◽  
Author(s):  
Joseph E. Langenderfer ◽  
Peter J. Laz ◽  
Anthony J. Petrella ◽  
Paul J. Rullkoetter
Keyword(s):  
Lower Extremity ◽  
Inverse Dynamics ◽  
The Body ◽  
Implant Design ◽  
Anatomical Landmarks ◽  
Body Segment ◽  
Joint Loading ◽  
Data Set ◽  
Reaction Data ◽  
Body Segment Parameters

Inverse dynamics is a standard approach for estimating joint loadings in the lower extremity from kinematic and ground reaction data for use in clinical and research gait studies. Variability in estimating body segment parameters and uncertainty in defining anatomical landmarks have the potential to impact predicted joint loading. This study demonstrates the application of efficient probabilistic methods to quantify the effect of uncertainty in these parameters and landmarks on joint loading in an inverse-dynamics model, and identifies the relative importance of the parameters and landmarks to the predicted joint loading. The inverse-dynamics analysis used a benchmark data set of lower-extremity kinematics and ground reaction data during the stance phase of gait to predict the three-dimensional intersegmental forces and moments. The probabilistic analysis predicted the 1–99 percentile ranges of intersegmental forces and moments at the hip, knee, and ankle. Variabilities, in forces and moments of up to 56% and 156% of the mean values were predicted based on coefficients of variation less than 0.20 for the body segment parameters and standard deviations of 2mm for the anatomical landmarks. Sensitivity factors identified the important parameters for the specific joint and component directions. Anatomical landmarks affected moments to a larger extent than body segment parameters. Additionally, for forces, anatomical landmarks had a larger effect than body segment parameters, with the exception of segment masses, which were important to the proximal-distal joint forces. The probabilistic modeling approach predicted the range of possible joint loading, which has implications in gait studies, clinical assessments, and implant design evaluations.

scholarly journals Estimating body segment parameters from three-dimensional human body scans

2021 ◽  
Author(s):  
Pawel Kudzia ◽  
Erika A. Jackson ◽  
Genevieve A. Dumas
Keyword(s):  
The Body ◽  
Parameter Estimates ◽  
Body Segment ◽  
Surface Scanning ◽  
Total Body Mass ◽  
3D Surface ◽  
Longitudinal Length ◽  
Mass Moment ◽  
Total Body ◽  
Body Segment Parameters

Body segment parameters are inputs for a range of applications. The estimation of body segment parameters that are participant-specific is desirable as it requires fewer prior assumptions and can reduce outcome measurement errors. Commonly used methods for estimating participant-specific body segment parameters are either expensive and out of reach (medical imaging), have many underlying assumptions (geometrical modelling) or are based on a specific subset of a population (regression models). Our objective was to develop a participant-specific 3D scanning and body segmentation method that estimates body segment parameters without any assumptions about the geometry of the body, ethnic background, and gender, is low-cost, fast, and can be readily available. Using a Microsoft Kinect camera, we developed a 3D surface scanning protocol that estimated participant-specific body segment parameters. To evaluate our system, we performed repeated 3D scans of 21 healthy participants (10 male, 11 female). We used open-source software to segment each body scan into 16 segments (head, torso, abdomen, pelvis, left and right hand, forearm, upper arm, foot, shank and thigh) and wrote custom software to estimate each segment's mass, mass moment of inertia in the three principal orthogonal axes relevant to the center of the segment, longitudinal length, and center of mass. We compared our body segment parameter estimates to those obtained using two comparison methods and found that our system was consistent in estimating total body volume between repeated scans (male p=0.1194, female p = 0.2240), estimated total body mass without significant differences when compared to our comparison method and a medical scale (male p=0.8529, female p = 0.6339), and generated consistent and comparable estimates across all of the body segment parameters of interest. The work here outlines an inexpensive 3D surface scanning approach for estimating a range of participant-specific body segment parameters.

Improving Net Joint Torque Calculations Through a Two-Step Optimization Method for Estimating Body Segment Parameters

2008 ◽  
Vol 131 (1) ◽  
Author(s):  
Raziel Riemer ◽  
Elizabeth T. Hsiao-Wecksler
Keyword(s):  
Inverse Dynamics ◽  
Optimization Problems ◽  
Joint Torque ◽  
Optimization Approach ◽  
Regression Equations ◽  
Random Errors ◽  
Body Segment ◽  
Joint Torques ◽  
Body Segment Parameters ◽  
True Values

Two main sources of error in inverse dynamics based calculations of net joint torques are inaccuracies in segmental motions and estimates of anthropometric body segment parameters (BSPs). Methods for estimating BSP (i.e., segmental moment of inertia, mass, and center of mass location) have been previously proposed; however, these methods are limited due to low accuracies, cumbersome use, need for expensive medical equipment, and∕or sensitivity of performance. This paper proposes a method for improving the accuracy of calculated net joint torques by optimizing for subject-specific BSP in the presence of characteristic and random errors in motion data measurements. A two-step optimization approach based on solving constrained nonlinear optimization problems was used. This approach minimized the differences between known ground reaction forces (GRFs), such as those measured by a force plate, and the GRF calculated via a top-down inverse dynamics approach. In step 1, a series of short calibration motions was used to compute first approximations of optimized segment motions and BSP for each motion. In step 2, refined optimal BSPs were derived from a combination of these motion profiles. We assessed the efficacy of this approach using a set of reference motions in which the true values for the BSP, segment motion, GRF, and net joint torques were known. To imitate real-world data, we introduced various noise conditions on the true motion and BSP data. We compared the root mean squared errors in calculated net joint torques relative to the true values due to the optimal BSP versus traditionally-derived BSP (from anthropometric tables derived from regression equations) and found that the optimized BSP reduced the error by 77%. These results suggest that errors in calculated net joint torques due to traditionally-derived BSP estimates could be reduced substantially using this optimization approach.

scholarly journals Estimating body segment parameters from three-dimensional human body scans

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262296
Author(s):  
Pawel Kudzia ◽  
Erika Jackson ◽  
Genevieve Dumas
Keyword(s):  
The Body ◽  
Body Segment ◽  
Surface Scanning ◽  
Total Body Mass ◽  
Scanning Method ◽  
3D Surface ◽  
Longitudinal Length ◽  
Mass Moment ◽  
Total Body ◽  
Body Segment Parameters

Body segment parameters are inputs for a range of applications. Participant-specific estimates of body segment parameters are desirable as this requires fewer prior assumptions and can reduce outcome measurement errors. Commonly used methods for estimating participant-specific body segment parameters are either expensive and out of reach (medical imaging), have many underlying assumptions (geometrical modelling) or are based on a specific subset of a population (regression models). Our objective was to develop a participant-specific 3D scanning and body segmentation method that estimates body segment parameters without any assumptions about the geometry of the body, ethnic background, and gender, is low-cost, fast, and can be readily available. Using a Microsoft Kinect Version 2 camera, we developed a 3D surface scanning protocol that enabled the estimation of participant-specific body segment parameters. To evaluate our system, we performed repeated 3D scans of 21 healthy participants (10 male, 11 female). We used open source tools to segment each body scan into 16 segments (head, torso, abdomen, pelvis, left and right hand, forearm, upper arm, foot, shank and thigh) and wrote custom software to estimate each segment’s mass, mass moment of inertia in the three principal orthogonal axes relevant to the center of the segment, longitudinal length, and center of mass. We compared our body segment parameter estimates to those obtained using two comparison methods and found that our system was consistent in estimating total body volume between repeated scans (male p = 0.1194, female p = 0.2240), estimated total body mass without significant differences when compared to our comparison method and a medical scale (male p = 0.8529, female p = 0.6339), and generated consistent and comparable estimates across a range of the body segment parameters of interest. Our work here outlines and provides the code for an inexpensive 3D surface scanning method for estimating a range of participant-specific body segment parameters.

scholarly journals Subject-specific body segment parameter estimation using 3D photogrammetry with multiple cameras

2014 ◽  
Author(s):  
Kathrin E Peyer ◽  
Mark Morris ◽  
William I Sellers
Keyword(s):  
Regression Models ◽  
Point Cloud ◽  
The Body ◽  
Reconstruction Algorithms ◽  
Multiple Cameras ◽  
Body Segment ◽  
Body Segments ◽  
Subject Specific ◽  
3D Photogrammetry ◽  
Body Segment Parameters

Inertial properties of body segments, such as mass, centre of mass or moments of inertia, are important parameters when studying movements of the human body. These quantities are, however, not directly measurable. Current approaches include using regression models which have limited accuracy; geometric models with lengthy measuring procedures; or acquiring and post-processing MRI scans of participants. We propose a geometric methodology based on 3D photogrammetry using multiple cameras to provide subject-specific body segment parameters while minimizing the interaction time with the participants. A low-cost body scanner was built using multiple cameras and 3D point cloud data generated using structure from motion photogrammetric reconstruction algorithms. The point cloud was manually separated into body segments and convex hulling applied to each segment to produce the required geometric outlines. The accuracy of the method can be adjusted by choosing the number of subdivisions of the body segments. The body segment parameters of six participants (four male and two female) are presented using the proposed method. The multi-camera photogrammetric approach is expected to be particularly suited for studies including populations for which regression models are not available in literature and where other geometric techniques or MRI scanning are not applicable due to time or ethical constraints.

An Automated Image-Based Method of 3D Subject Specific Body Segment Parameter Estimation

2008 ◽  
Author(s):  
Alison L. Sheets ◽  
Stefano Corazza ◽  
Thomas Andriacchi
Keyword(s):  
The Body ◽  
Regression Equations ◽  
Body Segment ◽  
Non Invasive ◽  
Subject Specific ◽  
Joint Center ◽  
The Right ◽  
Almost All ◽  
Body Segment Parameters ◽  
Application Specific

Recent studies have suggested that limb kinetics during swing or float phase movements are important for ACL injury analysis and injury prevention [1]. Kinetic (moment and force) calculations during swing phase can be sensitive to the accuracy of subject-specific body segment parameters (BSP) including mass and inertial properties. While numerous methods for estimating BSP have been implemented including regression equations [2,3], geometric body shape estimations, medical imaging and optimization approaches, they all have application specific limitations. Almost all of these BSP estimation approaches are limited by assumptions that: the mass center (CM) lies on the axis connecting the segment’s proximal and distal joint center, the body principle moments of inertia are aligned with the segment axes [4], and the right and left limbs are symmetric. These assumptions could introduce errors in 3D kinematic analysis. Non-invasive methods of measuring the exact geometry and volume of body segments have the potential to reduce most sources of error.

INFLUENCE OF BODY SEGMENT PARAMETERS ON INVERSE DYNAMICS AND ESTIMATED MUSCLE FORCES

2012 ◽  
Vol 45 ◽  
pp. S241
Author(s):  
Mariska Wesseling ◽  
Friedl De Groote ◽  
Christophe Meyer ◽  
Ilse Jonkers
Keyword(s):  
Inverse Dynamics ◽  
Muscle Forces ◽  
Body Segment ◽  
Body Segment Parameters

scholarly journals A low-cost three-dimensional laser surface scanning approach for defining body segment parameters

2017 ◽  
Vol 231 (11) ◽  
pp. 1064-1068 ◽  
Author(s):  
Petros Pandis ◽  
Anthony MJ Bull
Keyword(s):  
Low Cost ◽  
Three Dimensional ◽  
Dynamic Modelling ◽  
The Body ◽  
Parameter Estimates ◽  
Body Segment ◽  
Subject Specific ◽  
Actual Volume ◽  
System Body ◽  
Body Segment Parameters

Body segment parameters are used in many different applications in ergonomics as well as in dynamic modelling of the musculoskeletal system. Body segment parameters can be defined using different methods, including techniques that involve time-consuming manual measurements of the human body, used in conjunction with models or equations. In this study, a scanning technique for measuring subject-specific body segment parameters in an easy, fast, accurate and low-cost way was developed and validated. The scanner can obtain the body segment parameters in a single scanning operation, which takes between 8 and 10 s. The results obtained with the system show a standard deviation of 2.5% in volumetric measurements of the upper limb of a mannequin and 3.1% difference between scanning volume and actual volume. Finally, the maximum mean error for the moment of inertia by scanning a standard-sized homogeneous object was 2.2%. This study shows that a low-cost system can provide quick and accurate subject-specific body segment parameter estimates.

2-D Inverse Dynamics Knee Model: Aligning Anatomical Knee Model With Knee Extension Kinematic Data Using Ligament Forces

2018 ◽  
Author(s):  
Jose Mario Salinas ◽  
Dumitru I. Caruntu
Keyword(s):  
Experimental Data ◽  
Coordinate System ◽  
Inverse Dynamics ◽  
Knee Extension ◽  
The Body ◽  
Body Segment ◽  
Center Of Rotation ◽  
Axis Of Rotation ◽  
Instantaneous Center ◽  
Body Fixed Coordinate System

This paper deals with aligning knee geometrical anatomical data with kinematic data from experimental work in order to develop a two-dimensional inverse dynamics anatomical model of human knee. Motion capture cameras were used to collect the experimental data for a knee extension exercise. Reflective markers were placed on the subjects’ skin during the experiment. In this model, joints such as hip, knee, and ankle are represented by axes of rotation. These axes are determined by calculating the relative instantaneous center of rotation of one body segment with respect to an adjacent body segment. Body-fixed coordinate systems were defined using three reflective markers attached to the subject. The origin of each body fixed-coordinate system was located between the three markers on that body segment, the body-fixed x-axis was pointing towards the marker on the lateral side of the body segment, and the body-fixed y-axis fell on the same plane as the three reflective markers on the body segment. Moreover, the axis of rotation that represents the hip was determined by calculating the instantaneous center of rotation of reflective markers located on the pelvis with respect to a body fixed coordinate system on the thigh. The axis of rotation on the knee was determined by calculating the instantaneous center of rotation of reflective markers on the shin (tibia) with respect to the body-fixed coordinate system on the thigh (femur). The axis of rotation on the ankle was determined by calculating the instantaneous center of rotation of reflective markers on the shin with respect to a body-fixed coordinate system on the foot. Bone anatomical geometries of femur and tibia were represented mathematically as polynomials and superimposed over the experimental data. This was done by matching the center of rotation from experimental data with the geometric center of the femoral condyle. This is necessary for estimating the insertions/origins of knee ligaments. These ligaments are modeled as nonlinear elastic springs. Furthermore, ligaments were divided into separate fiber bundles. Both the posterior and anterior cruciate ligaments were divided into an anterior and posterior fiber bundle. The cruciate ligament forces for both exercises are discussed in this paper.

scholarly journals Location of the shoulder joint centre and identification of the body segment parameters

PAMM ◽  
2007 ◽  
Vol 7 (1) ◽  
pp. 3050005-3050006
Author(s):  
Tony Monnet ◽  
Kossi Atchonouglo ◽  
Claude Vallée
Keyword(s):  
Shoulder Joint ◽  
The Body ◽  
Body Segment ◽  
Body Segment Parameters

Comparative Analysis of Methods for Estimating Arm Segment Parameters and Joint Torques From Inverse Dynamics

2011 ◽  
Vol 133 (3) ◽  
Author(s):  
Davide Piovesan ◽  
Alberto Pierobon ◽  
Paul DiZio ◽  
James R. Lackner
Keyword(s):  
Root Mean Square ◽  
Upper Limb ◽  
Inverse Dynamics ◽  
Estimation Method ◽  
Joint Torque ◽  
Reaching Movements ◽  
Mean Square ◽  
Body Segment ◽  
Joint Torques ◽  
Body Segment Parameters

A common problem in the analyses of upper limb unfettered reaching movements is the estimation of joint torques using inverse dynamics. The inaccuracy in the estimation of joint torques can be caused by the inaccuracy in the acquisition of kinematic variables, body segment parameters (BSPs), and approximation in the biomechanical models. The effect of uncertainty in the estimation of body segment parameters can be especially important in the analysis of movements with high acceleration. A sensitivity analysis was performed to assess the relevance of different sources of inaccuracy in inverse dynamics analysis of a planar arm movement. Eight regression models and one water immersion method for the estimation of BSPs were used to quantify the influence of inertial models on the calculation of joint torques during numerical analysis of unfettered forward arm reaching movements. Thirteen subjects performed 72 forward planar reaches between two targets located on the horizontal plane and aligned with the median plane. Using a planar, double link model for the arm with a floating shoulder, we calculated the normalized joint torque peak and a normalized root mean square (rms) of torque at the shoulder and elbow joints. Statistical analyses quantified the influence of different BSP models on the kinetic variable variance for given uncertainty on the estimation of joint kinematics and biomechanical modeling errors. Our analysis revealed that the choice of BSP estimation method had a particular influence on the normalized rms of joint torques. Moreover, the normalization of kinetic variables to BSPs for a comparison among subjects showed that the interaction between the BSP estimation method and the subject specific somatotype and movement kinematics was a significant source of variance in the kinetic variables. The normalized joint torque peak and the normalized root mean square of joint torque represented valuable parameters to compare the effect of BSP estimation methods on the variance in the population of kinetic variables calculated across a group of subjects with different body types. We found that the variance of the arm segment parameter estimation had more influence on the calculated joint torques than the variance of the kinematics variables. This is due to the low moments of inertia of the upper limb, especially when compared with the leg. Therefore, the results of the inverse dynamics of arm movements are influenced by the choice of BSP estimation method to a greater extent than the results of gait analysis.

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