Are Patient-Specific Joint and Inertial Parameters Necessary for Accurate Inverse Dynamics Analyses of Gait?

Reinbolt, Jeffrey A.; Haftka, Raphael T.; Chmielewski, Terese L.; Fregly, Benjamin J.

doi:10.1109/tbme.2006.889187

Cited by 84 publications

(62 citation statements)

References 26 publications

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“…This property is then used to define the following indicator of the accuracy of the inverse dynamics step: the dynamic residuals indicator dr , defined as the root-mean-square (RMS) of λ 1 throughout the motion (7). To allow for inter-subject comparisons, the three effort components of dr are normalized by the body weight of the subject (m s .g) and the three torque components are normalized by the body weight times the height (m s .g.h s ) [15].…”

Section: Inverse Dynamicsmentioning

confidence: 99%

“…Then, a least squares method solved the optimization problem. Meanwhile, [15] and [16] focused on the 6 degrees of freedom (DoF) joint between the floating-base system and the global reference frame as a measure of the simulation accuracy. The optimization problem consisted in minimizing the generalized forces at this virtual joint, that corresponds to the dynamic residuals.…”

Section: Introductionmentioning

confidence: 99%

“…On one hand, during the walking motion, [15,19,20] analyzed the variation of the resulting joint torques introduced by the uncertainties of different dynamic inputs. On the other hand, [17] assessed different inverse kinematics methods -particularly the effect of the kinematic data consistency [21] -on inverse dynamics consistency.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty propagation in multibody human model dynamics

2017

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In biomechanics, calibration of body segment inertial parameters (BSIP) is crucial to take into account subject morphological specificities. To avoid strenuous protocols, identification methods based on rigid body dynamics laws have been proposed. Thanks to a motion capture system and force platforms, these methods optimize BSIP by minimizing errors in the equations of motion. These errors can be defined as the dynamic residuals reflecting inaccuracies arising from estimated BSIP, as well as from kinematics and force plate measurements. The current study aims at evaluating the part of uncertainty on the dynamic residuals directly related to kinematics and force plate measurements. To answer this question, we captured the movements of 10 participants performing a standardized motion. We then applied a Monte Carlo-based approach to introduce variations in the kinematics and force plate measurements, and evaluated the reconstructed difference on the dynamic residuals. Results show that, first, the BSIP evaluation using a regression method seemed to be an acceptable estimate for the studied subjects. Second, the part of uncertainty in the dynamic residuals was significantly higher than the dynamic residuals obtained. In conclusion, a subject-specific calibration of the BSIP based on dynamic residuals, for this model and protocol, seems irrelevant and prone to overfitting of BSIP.

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Section: Inverse Dynamicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Uncertainty propagation in multibody human model dynamics

2017

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“…After successful compilation steps 1, 2, and 3 on data, step to construct the human skeleton model by using the following function, seen in Figure 6. mcplotframe (walkm,180, mapar); (3) There are many methods [21][22][23][24][25][26][27][28][29][30][31] to compute the human body segment parameters. They have trendy source of BSP knowledge of human body.…”

Section: ) First Access the Index Of Motion Sensors Which Are Placedmentioning

confidence: 99%

A New Bio-Informatics Framework: Research on 3D Sensor Data of Human Activities

Ali¹,

Wu²,

Khan³

et al. 2015

ijacsa

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Abstract-Due to increasing attraction of motion capture systems technology and the usage of captured data in wide range of research-oriented applications, a framework has developed as an improved version of MOCAP TOOLBOX in Matlab platform. Firstly, we have introduced a faithful script to deal with public motion capture data, which will be friendly for us. Various functions through dynamic programming, by using the Body Segment Parameters (BSP) are edited and they configured the position of markers according to data. It is used to visualize and refine without the MLS view and the C3D editor software. It has opened a valuable way of sensor data in many research aspects as gait movements, marker analysis, compression and motion pattern, bioinformatics, and animation. As a result, performed on CMU and ACCAD public mocap data, and achieved higher corrected configuration scheme of 3D markers when compared with the prior art, especially for C3D file. Another distinction of this work is that it handles the extra markers distortion, and provides the meaningful way to use captured data.

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“…While some studies (Pàmies-Vilà et al, 2012;Reinbolt et al, 2007) suggest that errors in kinematic data influence joint moment estimates more than the errors in BSP estimates, others (Rao et al, 2006;Pearsall and Costigan, 1999) show that the influence of BSP errors cannot be neglected. In all the above cases, the sensitivity of the joint reactions to the perturbations in the kinematics and BSP are studied using the overdetermined inverse model.…”

Section: S Mohan Varma and S Sujatha: Segmental Contributions Tomentioning

confidence: 99%

Segmental contributions to the ground reaction force in the single support phase of gait

Varma

Sujatha

2014

Mech. Sci.

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Abstract. An inverse dynamics model for the single support (SS) phase of gait is developed to study segmental contributions to the ground reaction force (GRF). With segmental orientations as the generalized degrees of freedom (DOF), the acceleration of the body's center-of-mass is expressed analytically as the summation of the weighted kinematics of individual segments. The weighting functions are constants that are functions of the segment masses and center-of-mass distances. Using kinematic and anthropometric data from literature as inputs, and using the roll-over-shape (ROS) to model the foot-ground interaction, GRF obtained from the inverse model are compared with measured GRF data from literature. The choice of the generalized coordinates and mathematical form of the model provides a means to weigh individual segment contributions, simplify models and choose more kinetically accurate inverse dynamics models. For the kinematic data used, an anthropomorphic model that includes the frontal plane rotation of the pelvis in addition to the sagittal DOF of the thigh and shank most accurately captures the vertical component of the GRF in the SS phase of walking. Of the two ROS used, the ankle-foot roll-over shape provides a better approximation of the kinetics in the SS phase. The method presented here can be used with additional experimental studies to confirm these results.

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Are Patient-Specific Joint and Inertial Parameters Necessary for Accurate Inverse Dynamics Analyses of Gait?

Cited by 84 publications

References 26 publications

Uncertainty propagation in multibody human model dynamics

Uncertainty propagation in multibody human model dynamics

A New Bio-Informatics Framework: Research on 3D Sensor Data of Human Activities

Segmental contributions to the ground reaction force in the single support phase of gait

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