Validation of Knee Load Predictions During a Dual Limb Squat and Calfrise

2012 ◽  
Author(s):  
Trent M. Guess ◽  
Antonis Stylianou ◽  
Mohammad Kia
Keyword(s):  
Reaction Force ◽  
Contact Forces ◽  
Grand Challenge ◽  
Ground Reaction Forces ◽  
Data Set ◽  
Prosthetic Knee ◽  
Knee Loading ◽  
Model Predictions ◽  
Reaction Forces

Knowledge of knee loading would benefit prosthetic design, development of tissue engineered materials, orthopedic repair, and management of degenerative joint diseases such as osteoarthritis. Musculoskeletal modeling provides a method for estimating in vivo joint loading, but validation of model predictions is challenging. Data provided by the “Grand Challenge Competition to Predict In-Vivo Knee Loads” for the 2012 American Society of Mechanical Engineers Summer Bioengineering Conference [1] provides data from an instrumented prosthetic knee that can be used to validate load predictions. The Grand Challenge data set includes implant and bone geometries, motion, ground reaction forces, electromyography (EMG) as well as measured knee loading. Presented here are muscle driven forward dynamics simulations with a prosthetic knee for two of the calibration gait trials (SC_2legsquat and SC_calfrise) provided with the Grand Challenge data set. The calibration trials include the instrumented knee measurements and are provided to help “calibrate” models used in the Grand Challenge competition. Inputs to model simulations were experimental marker motion and outputs included muscle force, ground reaction forces, ligament forces, contact forces, and knee loading. Experimental measurements of knee loading, ground reaction force, and muscle activations were compared to model predictions.

Intra-Articular Knee Contact Force Estimation During Walking Using Force-Reaction Elements and Subject-Specific Joint Model2

2016 ◽  
Vol 138 (2) ◽  
Author(s):  
Yihwan Jung ◽  
Cong-Bo Phan ◽  
Seungbum Koo
Keyword(s):  
Contact Force ◽  
Inverse Dynamics ◽  
Reaction Force ◽  
Muscle Tension ◽  
Contact Forces ◽  
Contact Model ◽  
Grand Challenge ◽  
Subject Specific ◽  
Mean Square Errors

Joint contact forces measured with instrumented knee implants have not only revealed general patterns of joint loading but also showed individual variations that could be due to differences in anatomy and joint kinematics. Musculoskeletal human models for dynamic simulation have been utilized to understand body kinetics including joint moments, muscle tension, and knee contact forces. The objectives of this study were to develop a knee contact model which can predict knee contact forces using an inverse dynamics-based optimization solver and to investigate the effect of joint constraints on knee contact force prediction. A knee contact model was developed to include 32 reaction force elements on the surface of a tibial insert of a total knee replacement (TKR), which was embedded in a full-body musculoskeletal model. Various external measurements including motion data and external force data during walking trials of a subject with an instrumented knee implant were provided from the Sixth Grand Challenge Competition to Predict in vivo Knee Loads. Knee contact forces in the medial and lateral portions of the instrumented knee implant were also provided for the same walking trials. A knee contact model with a hinge joint and normal alignment could predict knee contact forces with root mean square errors (RMSEs) of 165 N and 288 N for the medial and lateral portions of the knee, respectively, and coefficients of determination (R2) of 0.70 and −0.63. When the degrees-of-freedom (DOF) of the knee and locations of leg markers were adjusted to account for the valgus lower-limb alignment of the subject, RMSE values improved to 144 N and 179 N, and R2 values improved to 0.77 and 0.37, respectively. The proposed knee contact model with subject-specific joint model could predict in vivo knee contact forces with reasonable accuracy. This model may contribute to the development and improvement of knee arthroplasty.

scholarly journals A data set with kinematic and ground reaction forces of human balance

2017 ◽  
Author(s):  
Damiana A dos Santos ◽  
Claudiane A Fukuchi ◽  
Reginaldo K Fukuchi ◽  
Marcos Duarte
Keyword(s):  
Center Of Pressure ◽  
Reaction Force ◽  
Kinematic Model ◽  
Three Dimensional ◽  
Force Platform ◽  
Whole Body ◽  
Ground Reaction Forces ◽  
Data Set ◽  
Public Data ◽  
Reaction Forces

This article describes a public data set with the three-dimensional kinematics of the whole body and the ground reaction forces (with a dual force platform setup) of subjects standing still for 60 s in different conditions, in which the vision and the standing surface were manipulated. Twenty-seven young subjects and 22 old subjects were evaluated. The data set comprises a file with metadata plus 1,813 files with the ground reaction force (GRF) and kinematics data for the 49 subjects (three files for each of the 12 trials plus one file for each subject). The file with metadata has information about each subject’s sociocultural, demographic, and health characteristics. The files with the GRF have the data from each force platform and from the resultant GRF (including the center of pressure data). The files with the kinematics have the three-dimensional position of the 42 markers used for the kinematic model of the whole body and the 73 calculated angles. In this text, we illustrate how to access, analyze, and visualize the data set. All the data is available at Figshare (DOI: 10.6084/m9.figshare.4525082 ), and a companion Jupyter Notebook (available at https://github.com/demotu/datasets ) presents the programming code to generate analyses and other examples.

scholarly journals A data set with kinematic and ground reaction forces of human balance

2017 ◽  
Author(s):  
Damiana A dos Santos ◽  
Claudiane A Fukuchi ◽  
Reginaldo K Fukuchi ◽  
Marcos Duarte
Keyword(s):  
Center Of Pressure ◽  
Reaction Force ◽  
Kinematic Model ◽  
Three Dimensional ◽  
Force Platform ◽  
Whole Body ◽  
Ground Reaction Forces ◽  
Data Set ◽  
Public Data ◽  
Reaction Forces

This article describes a public data set with the three-dimensional kinematics of the whole body and the ground reaction forces (with a dual force platform setup) of subjects standing still for 60 s in different conditions, in which the vision and the standing surface were manipulated. Twenty-seven young subjects and 22 old subjects were evaluated. The data set comprises a file with metadata plus 1,813 files with the ground reaction force (GRF) and kinematics data for the 49 subjects (three files for each of the 12 trials plus one file for each subject). The file with metadata has information about each subject’s sociocultural, demographic, and health characteristics. The files with the GRF have the data from each force platform and from the resultant GRF (including the center of pressure data). The files with the kinematics have the three-dimensional position of the 42 markers used for the kinematic model of the whole body and the 73 calculated angles. In this text, we illustrate how to access, analyze, and visualize the data set. All the data is available at Figshare (DOI: 10.6084/m9.figshare.4525082 ), and a companion Jupyter Notebook (available at https://github.com/demotu/datasets ) presents the programming code to generate analyses and other examples.

scholarly journals An Electromyogram-Driven Musculoskeletal Model of the Knee to Predict in Vivo Joint Contact Forces During Normal and Novel Gait Patterns

2013 ◽  
Vol 135 (2) ◽  
Author(s):  
Kurt Manal ◽  
Thomas S. Buchanan
Keyword(s):  
Knee Joint ◽  
Contact Force ◽  
Contact Forces ◽  
Grand Challenge ◽  
Normal Walking ◽  
Gait Patterns ◽  
Internal Joint ◽  
Model Predictions ◽  
Joint Contact

Computational models that predict internal joint forces have the potential to enhance our understanding of normal and pathological movement. Validation studies of modeling results are necessary if such models are to be adopted by clinicians to complement patient treatment and rehabilitation. The purposes of this paper are: (1) to describe an electromyogram (EMG)-driven modeling approach to predict knee joint contact forces, and (2) to evaluate the accuracy of model predictions for two distinctly different gait patterns (normal walking and medial thrust gait) against known values for a patient with a force recording knee prosthesis. Blinded model predictions and revised model estimates for knee joint contact forces are reported for our entry in the 2012 Grand Challenge to predict in vivo knee loads. The EMG-driven model correctly predicted that medial compartment contact force for the medial thrust gait increased despite the decrease in knee adduction moment. Model accuracy was high: the difference in peak loading was less than 0.01 bodyweight (BW) with an R2 = 0.92. The model also predicted lateral loading for the normal walking trial with good accuracy exhibiting a peak loading difference of 0.04 BW and an R2 = 0.44. Overall, the EMG-driven model captured the general shape and timing of the contact force profiles and with accurate input data the model estimated joint contact forces with sufficient accuracy to enhance the interpretation of joint loading beyond what is possible from data obtained from standard motion capture studies.

The Stationary Configuration of the Knee

2013 ◽  
Vol 103 (2) ◽  
pp. 126-135 ◽  
Author(s):  
Wangdo Kim ◽  
Antonio P. Veloso ◽  
Veronica E. Vleck ◽  
Carlos Andrade ◽  
Sean S. Kohles
Keyword(s):  
Knee Joint ◽  
Inverse Dynamics ◽  
Reaction Force ◽  
Reciprocal System ◽  
Grand Challenge ◽  
Joint Movement ◽  
Structural Components ◽  
Data Set ◽  
And Cartilage

Background: Ligaments and cartilage contact contribute to the mechanical constraints in the knee joints. However, the precise influence of these structural components on joint movement, especially when the joint constraints are computed using inverse dynamics solutions, is not clear. Methods: We present a mechanical characterization of the connections between the infinitesimal twist of the tibia and the femur due to restraining forces in the specific tissue components that are engaged and responsible for such motion. These components include the anterior cruciate, posterior cruciate, medial collateral, and lateral collateral ligaments and cartilage contact surfaces in the medial and lateral compartments. Their influence on the bony rotation about the instantaneous screw axis is governed by restraining forces along the constraints explored using the principle of reciprocity. Results: Published kinetic and kinematic joint data (American Society of Mechanical Engineers Grand Challenge Competition to Predict In Vivo Knee Loads) are applied to define knee joint function for verification using an available instrumented knee data set. We found that the line of the ground reaction force (GRF) vector is very close to the axis of the knee joint. It aligns the knee joint with the GRF such that the reaction torques are eliminated. The reaction to the GRF will then be carried by the structural components of the knee instead. Conclusions: The use of this reciprocal system introduces a new dimension of foot loading to the knee axis alignment. This insight shows that locating knee functional axes is equivalent to the static alignment measurement. This method can be used for the optimal design of braces and orthoses for conservative treatment of knee osteoarthritis. (J Am Podiatr Med Assoc 103(2): 126–135, 2013)

scholarly journals A data set with kinematic and ground reaction forces of human balance

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3626 ◽  
Author(s):  
Damiana A. dos Santos ◽  
Claudiane A. Fukuchi ◽  
Reginaldo K. Fukuchi ◽  
Marcos Duarte
Keyword(s):  
Center Of Pressure ◽  
Reaction Force ◽  
Three Dimensional ◽  
Force Platform ◽  
Ground Reaction Forces ◽  
Data Set ◽  
Public Data ◽  
Human Balance ◽  
Reaction Forces ◽  
Young Subjects

This article describes a public data set containing the three-dimensional kinematics of the whole human body and the ground reaction forces (with a dual force platform setup) of subjects who were standing still for 60 s in different conditions, in which the subjects’ vision and the standing surface were manipulated. Twenty-seven young subjects and 22 old subjects were evaluated. The data set comprises a file with metadata plus 1,813 files with the ground reaction force (GRF) and kinematics data for the 49 subjects (three files for each of the 12 trials plus one file for each subject). The file with metadata has information about each subject’s sociocultural, demographic, and health characteristics. The files with the GRF have the data from each force platform and from the resultant GRF (including the center of pressure data). The files with the kinematics contain the three-dimensional positions of 42 markers that were placed on each subject’s body and 73 calculated joint angles. In this text, we illustrate how to access, analyze, and visualize the data set. All the data is available at Figshare (DOI:10.6084/m9.figshare.4525082), and a companion Jupyter Notebook presents programming code to access the data set, generate analyses and other examples. The availability of a public data set on the Internet that contains these measurements and information about how to access and process this data can potentially boost the research on human postural control, increase the reproducibility of studies, and be used for training and education, among other applications.

scholarly journals Update on Grand Challenge Competition to Predict in Vivo Knee Loads

2013 ◽  
Vol 135 (2) ◽  
Author(s):  
Allison L. Kinney ◽  
Thor F. Besier ◽  
Darryl D. D'Lima ◽  
Benjamin J. Fregly
Keyword(s):  
Contact Force ◽  
Contact Forces ◽  
Single Subject ◽  
Grand Challenge ◽  
Muscle Forces ◽  
Data Set ◽  
Lateral Contact ◽  
Musculoskeletal Models ◽  
Rms Errors

Validation is critical if clinicians are to use musculoskeletal models to optimize treatment of individual patients with a variety of musculoskeletal disorders. This paper provides an update on the annual Grand Challenge Competition to Predict in Vivo Knee Loads, a unique opportunity for direct validation of knee contact forces and indirect validation of knee muscle forces predicted by musculoskeletal models. Three competitions (2010, 2011, and 2012) have been held at the annual American Society of Mechanical Engineers Summer Bioengineering Conference, and two more competitions are planned for the 2013 and 2014 conferences. Each year of the competition, a comprehensive data set collected from a single subject implanted with a force-measuring knee replacement is released. Competitors predict medial and lateral knee contact forces for two gait trials without knowledge of the experimental knee contact force measurements. Predictions are evaluated by calculating root-mean-square (RMS) errors and R2 values relative to the experimentally measured medial and lateral contact forces. For the first three years of the competition, competitors used a variety of methods to predict knee contact and muscle forces, including static and dynamic optimization, EMG-driven models, and parametric numerical models. Overall, errors in predicted contact forces were comparable across years, with average RMS errors for the four competition winners ranging from 229 N to 312 N for medial contact force and from 238 N to 326 N for lateral contact force. Competitors generally predicted variations in medial contact force (highest R2 = 0.91) better than variations in lateral contact force (highest R2 = 0.70). Thus, significant room for improvement exists in the remaining two competitions. The entire musculoskeletal modeling community is encouraged to use the competition data and models for their own model validation efforts.

scholarly journals Running ground reaction forces across footwear conditions are predicted from the motion of two body mass components

2019 ◽  
Vol 126 (5) ◽  
pp. 1315-1325 ◽  
Author(s):  
Andrew B. Udofa ◽  
Kenneth P. Clark ◽  
Laurence J. Ryan ◽  
Peter G. Weyand
Keyword(s):  
Ground Reaction Force ◽  
Lower Limb ◽  
Reaction Force ◽  
Ground Reaction Forces ◽  
Vertical Ground Reaction Force ◽  
Time Patterns ◽  
Foot Strike ◽  
Reaction Forces ◽  
Vertical Ground ◽  
Force Time

Although running shoes alter foot-ground reaction forces, particularly during impact, how they do so is incompletely understood. Here, we hypothesized that footwear effects on running ground reaction force-time patterns can be accurately predicted from the motion of two components of the body’s mass (mb): the contacting lower-limb (m1 = 0.08mb) and the remainder (m2 = 0.92mb). Simultaneous motion and vertical ground reaction force-time data were acquired at 1,000 Hz from eight uninstructed subjects running on a force-instrumented treadmill at 4.0 and 7.0 m/s under four footwear conditions: barefoot, minimal sole, thin sole, and thick sole. Vertical ground reaction force-time patterns were generated from the two-mass model using body mass and footfall-specific measures of contact time, aerial time, and lower-limb impact deceleration. Model force-time patterns generated using the empirical inputs acquired for each footfall matched the measured patterns closely across the four footwear conditions at both protocol speeds ( r2 = 0.96 ± 0.004; root mean squared error  = 0.17 ± 0.01 body-weight units; n = 275 total footfalls). Foot landing angles (θF) were inversely related to footwear thickness; more positive or plantar-flexed landing angles coincided with longer-impact durations and force-time patterns lacking distinct rising-edge force peaks. Our results support three conclusions: 1) running ground reaction force-time patterns across footwear conditions can be accurately predicted using our two-mass, two-impulse model, 2) impact forces, regardless of foot strike mechanics, can be accurately quantified from lower-limb motion and a fixed anatomical mass (0.08mb), and 3) runners maintain similar loading rates (ΔFvertical/Δtime) across footwear conditions by altering foot strike angle to regulate the duration of impact. NEW & NOTEWORTHY Here, we validate a two-mass, two-impulse model of running vertical ground reaction forces across four footwear thickness conditions (barefoot, minimal, thin, thick). Our model allows the impact portion of the impulse to be extracted from measured total ground reaction force-time patterns using motion data from the ankle. The gait adjustments observed across footwear conditions revealed that runners maintained similar loading rates across footwear conditions by altering foot strike angles to regulate the duration of impact.

scholarly journals Ground Reaction Forces and Kinematics of Ski Jump Landing Using Wearable Sensors

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2011 ◽  
Author(s):  
Bessone ◽  
Petrat ◽  
Schwirtz
Keyword(s):  
Wearable Sensors ◽  
Reaction Force ◽  
Ground Reaction Forces ◽  
Inertial Motion ◽  
Jump Landing ◽  
The Past ◽  
Landing Biomechanics ◽  
Reaction Forces ◽  
The Hill ◽  
Kinematics And Kinetics

In the past, technological issues limited research focused on ski jump landing. Today, thanks to the development of wearable sensors, it is possible to analyze the biomechanics of athletes without interfering with their movements. The aims of this study were twofold. Firstly, the quantification of the kinetic magnitude during landing is performed using wireless force insoles while 22 athletes jumped during summer training on the hill. In the second part, the insoles were combined with inertial motion units (IMUs) to determine the possible correlation between kinematics and kinetics during landing. The maximal normal ground reaction force (GRFmax) ranged between 1.1 and 5.3 body weight per foot independently when landing using the telemark or parallel leg technique. The GRFmax and impulse were correlated with flying time (p < 0.001). The hip flexions/extensions and the knee and hip rotations of the telemark front leg correlated with GRFmax (r = 0.689, p = 0.040; r = −0.670, p = 0.048; r = 0.820, p = 0.007; respectively). The force insoles and their combination with IMUs resulted in promising setups to analyze landing biomechanics and to provide in-field feedback to the athletes, being quick to place and light, without limiting movement.

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