Analysis of different uncertainties in the inverse dynamic analysis of human gait

Vilà, Rosa Pàmies; Font-Llagunes, Josep M.; Cuadrado, Javier; Sánchez, Francisco Javier Alonso

doi:10.1016/j.mechmachtheory.2012.07.010

Cited by 45 publications

(27 citation statements)

References 27 publications

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“…Traditional marker-based motion capture systems generally have system errors of 1-5 mm [35], which is combined with soft tissue artifacts of up to 10 mm for human movement [36]. Joint torques calculated from inverse dynamics have been shown to be sensitive to both force plate recordings and kinematics [37,38]. Thus, motion capture systems must be regularly and carefully calibrated and you should clearly document all steps of your experimental protocol (e.g., with written notes, photos, and videos).…”

Section: Validating and Evaluating Robustness Of Multibodymentioning

confidence: 99%

Is My Model Good Enough? Best Practices for Verification and Validation of Musculoskeletal Models and Simulations of Movement

Hicks

Uchida

Seth

et al. 2015

Journal of Biomechanical Engineering

522

455

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Computational modeling and simulation of neuromusculoskeletal (NMS) systems enables researchers and clinicians to study the complex dynamics underlying human and animal movement. NMS models use equations derived from physical laws and biology to help solve challenging real-world problems, from designing prosthetics that maximize running speed to developing exoskeletal devices that enable walking after a stroke. NMS modeling and simulation has proliferated in the biomechanics research community over the past 25 years, but the lack of verification and validation standards remains a major barrier to wider adoption and impact. The goal of this paper is to establish practical guidelines for verification and validation of NMS models and simulations that researchers, clinicians, reviewers, and others can adopt to evaluate the accuracy and credibility of modeling studies. In particular, we review a general process for verification and validation applied to NMS models and simulations, including careful formulation of a research question and methods, traditional verification and validation steps, and documentation and sharing of results for use and testing by other researchers. Modeling the NMS system and simulating its motion involves methods to represent neural control, musculoskeletal geometry, muscle-tendon dynamics, contact forces, and multibody dynamics. For each of these components, we review modeling choices and software verification guidelines; discuss variability, errors, uncertainty, and sensitivity relationships; and provide recommendations for verification and validation by comparing experimental data and testing robustness. We present a series of case studies to illustrate key principles. In closing, we discuss challenges the community must overcome to ensure that modeling and simulation are successfully used to solve the broad spectrum of problems that limit human mobility.

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Section: Validating and Evaluating Robustness Of Multibodymentioning

confidence: 99%

Is My Model Good Enough? Best Practices for Verification and Validation of Musculoskeletal Models and Simulations of Movement

Hicks

Uchida

Seth

et al. 2015

Journal of Biomechanical Engineering

522

455

View full text Add to dashboard Cite

show abstract

“…It is well-known that the results of IDA are sensitive to inaccuracies in these input data [14,15]. In addition, when analysing full-body models, the system becomes over-determinate as the GRF&Ms are input to the equations of motion [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Prediction of ground reaction forces and moments during sports-related movements

et al. 2016

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“…For example, inverse dynamics-based simulations are sensitive to inaccuracies in the measured kinematic and kinetic and data (Pàmies-Vilà et al, 2012), and the resulting dynamic inconsistency can lead to unrealistic model predictions (Kuo, 1998). Deriving the force plates data from three-dimensional full-body motion (Robert et al, 2013;Fluit et al, 2014a) represents a promising technique to both improve the dynamic consistency as well as remove the model's dependency on measured external forces.…”

Section: Ligament # Elementsmentioning

confidence: 99%

TLEM 2.0 – A comprehensive musculoskeletal geometry dataset for subject-specific modeling of lower extremity

Carbone

Fluit

Pellikaan

et al. 2015

Journal of Biomechanics

148

152

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When analyzing complex biomechanical problems such as predicting the effects of orthopedic surgery, subject-specific musculoskeletal models are essential to achieve reliable predictions. The aim of this paper is to present the Twente Lower Extremity Model 2.0, a new comprehensive dataset of the musculoskeletal geometry of the lower extremity, which is based on medical imaging data and dissection performed on the right lower extremity of a fresh male cadaver. Bone, muscle and subcutaneous fat (including skin) volumes were segmented from computed tomography and magnetic resonance images scans. Inertial parameters were estimated from the image-based segmented volumes. A complete cadaver dissection was performed, in which bony landmarks, attachments sites and lines-of-action of 55 muscle actuators and 12 ligaments, bony wrapping surfaces, and joint geometry were measured. The obtained musculoskeletal geometry dataset was finally implemented in the AnyBody Modeling System (AnyBody Technology A/S, Aalborg, Denmark), resulting in a model consisting of 12 segments, 11 joints and 21 degrees of freedom, and including 166 muscle-tendon elements for each leg. The new TLEM 2.0 dataset was purposely built to be easily combined with novel image-based scaling techniques, such as bone surface morphing, muscle volume registration and muscle-tendon path identification, in order to obtain subject-specific musculoskeletal models in a quick and accurate way. The complete dataset, including CT and MRI scans and segmented volume and surfaces, is made available at http://www.utwente.nl/ctw/bw/research/projects/TLEMsafe for the biomechanical community, in order to accelerate the development and adoption of subject-specific models on large scale. TLEM 2.0 is freely shared for non-commercial use only, under acceptance of the TLEMsafe Research License Agreement.

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Analysis of different uncertainties in the inverse dynamic analysis of human gait

Cited by 45 publications

References 27 publications

Is My Model Good Enough? Best Practices for Verification and Validation of Musculoskeletal Models and Simulations of Movement

Is My Model Good Enough? Best Practices for Verification and Validation of Musculoskeletal Models and Simulations of Movement

Prediction of ground reaction forces and moments during sports-related movements

TLEM 2.0 – A comprehensive musculoskeletal geometry dataset for subject-specific modeling of lower extremity

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