0
Technical Forum

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

[+] Author and Article Information
Jennifer L. Hicks

Department of Bioengineering,
Stanford University,
Stanford, CA 94305
e-mail: jenhicks@stanford.edu

Thomas K. Uchida, Ajay Seth

Department of Bioengineering,
Stanford University,
Stanford, CA 94305

Apoorva Rajagopal

Department of Mechanical Engineering,
Stanford University,
Stanford, CA 94305

Scott L. Delp

Department of Bioengineering and the
Department of Mechanical Engineering,
Stanford University,
Stanford, CA 94305

1Corresponding author.

Manuscript received September 10, 2014; final manuscript received December 2, 2014; published online January 26, 2015. Editor: Beth A. Winkelstein.

J Biomech Eng 137(2), 020905 (Feb 01, 2015) (24 pages) Paper No: BIO-14-1452; doi: 10.1115/1.4029304 History: Received September 10, 2014; Revised December 02, 2014; Online January 26, 2015

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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Figures

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Fig. 1

Publications per year related to biomechanical or musculoskeletal modeling or simulation. Statistics were generated by using Google Scholar to search publication titles and abstracts for the terms “biomechanical model”, “musculoskeletal model”, “biomechanical simulation”, or “musculoskeletal simulation.” The line represents a smoothed interpolation between averages computed in 5-year increments.

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Fig. 2

Overview of the verification and validation process. We begin a study by defining a research question and hypothesis. Proceeding clockwise, we then prototype the study methods and perform verification to ensure our computational model has been implemented correctly. We next perform simulations and validate the results against independent data to ensure the model and simulation faithfully represent the physical phenomena of interest. Only then can real-world predictions be generated, the robustness of which we must test to determine applicability as model parameters and inputs vary. These real-world predictions often suggest new research questions, beginning the cycle once more. Verifying software, validating simulation results, and testing the robustness of predictions form the core of the verification and validation process, and often lead to iteration as the study is refined. Documenting and sharing models and simulations ensures that results can be confirmed and extended by others.

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Fig. 3

Introduction to verification and validation case studies

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Fig. 4

Elements of a musculoskeletal simulation. A model of the NMS system can include computational models of muscle–tendon dynamics; geometry of bodies, joints, and muscles; models or estimates of contact; and models or estimates of neural control. A multibody dynamics engine is used to integrate the model's governing dynamic equations forward in time or solve for underlying motion and forces in an inverse analysis.

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Fig. 5

Case study—dynamic consistency and residuals

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Fig. 6

Case study—choosing and validating a musculoskeletal model

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Fig. 7

Verification test to ensure power from active and passive muscle fiber and tendon is equal to whole muscle actuator power. We generated a simulation with a constant muscle excitation of 0.6 (u), an initial block position of 0 m (x), and an initial block speed of 1 m/s (x·). We terminated the simulation after 0.5 s (t). The stacked area graph shows the summed power in the active muscle fiber (blue), passive muscle fiber (red), and tendon (green); the total muscle power is equal to the summation of these constituent powers (dashed black line).

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Fig. 8

Case study—tendon compliance sensitivity analysis

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Fig. 9

Case study—constraint-based contact modeling

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Fig. 10

Case study—comparing simulated muscle activations to EMG

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