Dynamic simulations of movement allow one to study neuromuscular coordination, analyze athletic performance, and estimate internal loading of the musculoskeletal system. Simulations can also be used to identify the sources of pathological movement and establish a scientific basis for treatment planning. We have developed a freely available, open-source software system (OpenSim) that lets users develop models of musculoskeletal structures and create dynamic simulations of a wide variety of movements. We are using this system to simulate the dynamics of individuals with pathological gait and to explore the biomechanical effects of treatments. OpenSim provides a platform on which the biomechanics community can build a library of simulations that can be exchanged, tested, analyzed, and improved through a multi-institutional collaboration. Developing software that enables a concerted effort from many investigators poses technical and sociological challenges. Meeting those challenges will accelerate the discovery of principles that govern movement control and improve treatments for individuals with movement pathologies.

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OpenSim: Open-Source Software to Create and Analyze Dynamic Simulations of Movement

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Biomechanics And Motor Control Of Human Movement

Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. As a clinical tool applied in the rehabilitation and diagnosis of medical conditions and sport activities, gait analysis using wearable sensors shows great prospects. The current paper reviews available wearable sensors and ambulatory gait analysis methods based on the various wearable sensors. After an introduction of the gait phases, the principles and features of wearable sensors used in gait analysis are provided. The gait analysis methods based on wearable sensors is divided into gait kinematics, gait kinetics, and electromyography. Studies on the current methods are reviewed, and applications in sports, rehabilitation, and clinical diagnosis are summarized separately. With the development of sensor technology and the analysis method, gait analysis using wearable sensors is expected to play an increasingly important role in clinical applications.

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Gait analysis using wearable sensors.

http://vic.crs4.it/vic/3DAHSummerSchool/Damsgaard.pdf

Analysis of Musculoskeletal Systems in the AnyBody Modeling System

https://engagedscholarship.csuohio.edu/cgi/viewcontent.cgi?article=1190&context=enme_facpub

Model-based estimation of muscle forces exerted during movements

A generic detailed rigid-body lumbar spine model

Study Design. This article describes the development of a musculoskeletal model of the human lumbar spine with focus on back muscles. It includes data from literature in a structured form. Objective. To review the anatomy and biomechanics of the back muscles related to the lumbar spine with relevance for biomechanical modeling. Summary of Background Data. To reduce complexity, muscle units have been incorporated in an abridged manner, reducing their actions more or less to a single force equivalent. In early models of the lumbar spine, this may have been a necessary step to reduce complexity and, thereby, calculation time. The muscles of the spine are well described in the literature, but mainly qualitatively. Most of the literature provides a description of the structures without precise data of fiber length, muscle length, cross-sectional areas, moment arms, forces, etc. The predicted output of musculoskeletal models is very much dependent on the input parameters. The information needed to improve models consists of better approximations of the attachments to the vertebrae, and more precise data. Method. Review of literature. Results. The predicted output of musculoskeletal models is very much dependent on the input parameters. Moderate changes in the assumed muscle line-of-action (i.e., moment arm) could substantially alter the magnitudes of predicted muscle and spinal forces, while the choice of optimization formulation is less sensitive. Conclusions. Input parameters, moment arms, as well as physiologic cross-sectional areas have a profound effect on the predicted muscle forces. Therefore, it is important to choose the values for moment arm and physiologic cross-sectional area carefully because they are essential input parameters to biomechanical models. (Less)

Anatomy and biomechanics of the back muscles in the lumbar spine with reference to biomechanical modeling.

Ground reaction forces and moments (GRF&M) are important measures used as input in biomechanical analysis to estimate joint kinetics, which often are used to infer information for many musculoskeletal diseases. Their assessment is conventionally achieved using laboratory-based equipment that cannot be applied in daily life monitoring. In this study, we propose a method to predict GRF&M during walking, using exclusively kinematic information from fully-ambulatory inertial motion capture (IMC). From the equations of motion, we derive the total external forces and moments. Then, we solve the indeterminacy problem during double stance using a distribution algorithm based on a smooth transition assumption. The agreement between the IMC-predicted and reference GRF&M was categorized over normal walking speed as excellent for the vertical (ρ = 0.992, rRMSE = 5.3%), anterior (ρ = 0.965, rRMSE = 9.4%) and sagittal (ρ = 0.933, rRMSE = 12.4%) GRF&M components and as strong for the lateral (ρ = 0.862, rRMSE = 13.1%), frontal (ρ = 0.710, rRMSE = 29.6%), and transverse GRF&M (ρ = 0.826, rRMSE = 18.2%). Sensitivity analysis was performed on the effect of the cut-off frequency used in the filtering of the input kinematics, as well as the threshold velocities for the gait event detection algorithm. This study was the first to use only inertial motion capture to estimate 3D GRF&M during gait, providing comparable accuracy with optical motion capture prediction. This approach enables applications that require estimation of the kinetics during walking outside the gait laboratory.

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Estimation of Ground Reaction Forces and Moments During Gait Using Only Inertial Motion Capture

Subject-specific scaling of cadaver-based musculoskeletal models is important for accurate musculoskeletal analysis within multiple areas such as ergonomics, orthopaedics and occupational health. We present two procedures to scale ‘generic’ musculoskeletal models to match segment lengths and joint parameters to a specific subject and compare the results to a simpler approach based on linear, segment-wise scaling. By incorporating data from functional and standing reference trials, the new scaling approaches reduce the model sensitivity to assumed model marker positions. For validation, we applied all three scaling methods to an inverse dynamics-based musculoskeletal model and compared predicted knee joint contact forces to those measured with an instrumented prosthesis during gait. Additionally, a Monte Carlo study was used to investigate the sensitivity of the knee joint contact force to random adjustments of the assumed model marker positions (+/− one marker diameter). The model based on linear scaling ...

/pdf/scaling-of-musculoskeletal-models-from-static-and-dynamic-3ld5yrtkxn.pdf

Mark de Zee

Papers

Analysis of Musculoskeletal Systems in the AnyBody Modeling System

A generic detailed rigid-body lumbar spine model

Anatomy and biomechanics of the back muscles in the lumbar spine with reference to biomechanical modeling.

Estimation of Ground Reaction Forces and Moments During Gait Using Only Inertial Motion Capture

Scaling of musculoskeletal models from static and dynamic trials