Movement is fundamental to human and animal life, emerging through interaction of complex neural, muscular, and skeletal systems. Study of movement draws from and contributes to diverse fields, including biology, neuroscience, mechanics, and robotics. OpenSim unites methods from these fields to create fast and accurate simulations of movement, enabling two fundamental tasks. First, the software can calculate variables that are difficult to measure experimentally, such as the forces generated by muscles and the stretch and recoil of tendons during movement. Second, OpenSim can predict novel movements from models of motor control, such as kinematic adaptations of human gait during loaded or inclined walking. Changes in musculoskeletal dynamics following surgery or due to human–device interaction can also be simulated; these simulations have played a vital role in several applications, including the design of implantable mechanical devices to improve human grasping in individuals with paralysis. OpenSim is an extensible and user-friendly software package built on decades of knowledge about computational modeling and simulation of biomechanical systems. OpenSim’s design enables computational scientists to create new state-of-the-art software tools and empowers others to use these tools in research and clinical applications. OpenSim supports a large and growing community of biomechanics and rehabilitation researchers, facilitating exchange of models and simulations for reproducing and extending discoveries. Examples, tutorials, documentation, and an active user forum support this community. The OpenSim software is covered by the Apache License 2.0, which permits its use for any purpose including both nonprofit and commercial applications. The source code is freely and anonymously accessible on GitHub, where the community is welcomed to make contributions. Platform-specific installers of OpenSim include a GUI and are available on simtk.org.

Muscle-driven simulations of human and animal motion are widely used to complement physical experiments for studying movement dynamics. Musculotendon models are an essential component of muscle-driven simulations, yet neither the computational speed nor the biological accuracy of the simulated forces has been adequately evaluated. Here we compare the speed and accuracy of three musculotendon models: two with an elastic tendon (an equilibrium model and a damped equilibrium model) and one with a rigid tendon. Our simulation benchmarks demonstrate that the equilibrium and damped equilibrium models produce similar force profiles but have different computational speeds. At low activation, the damped equilibrium model is 29 times faster than the equilibrium model when using an explicit integrator and 3 times faster when using an implicit integrator; at high activation, the two models have similar simulation speeds. In the special case of simulating a muscle with a short tendon, the rigid-tendon model produces forces that match those generated by the elastic-tendon models, but simulates 2-54 times faster when an explicit integrator is used and 6-31 times faster when an implicit integrator is used. The equilibrium, damped equilibrium, and rigid-tendon models reproduce forces generated by maximally-activated biological muscle with mean absolute errors less than 8.9%, 8.9%, and 20.9% of the maximum isometric muscle force, respectively. When compared to forces generated by submaximally-activated biological muscle, the forces produced by the equilibrium, damped equilibrium, and rigid-tendon models have mean absolute errors less than 16.2%, 16.4%, and 18.5%, respectively. To encourage further development of musculotendon models, we provide implementations of each of these models in OpenSim version 3.1 and benchmark data online, enabling others to reproduce our results and test their models of musculotendon dynamics.

SUMMARYThe lengths and velocities of muscle fibers have a dramatic effect on muscle force generation. It is unknown, however, whether the lengths and velocities of lower limb muscle fibers substantially affect the ability of muscles to generate force during walking and running. We examined this issue by developing simulations of muscle-tendon dynamics to calculate the lengths and velocities of muscle fibers from electromyographic recordings of 11 lower limb muscles and kinematic measurements of the hip, knee and ankle made as five subjects walked at speeds of 1.0-1.75ms-1 and ran at speeds of 2.0-5.0ms. We analyzed the simulated fiber lengths, fiber velocities and forces to evaluate the influence of force-length and force-velocity properties on force generation at different walking and running speeds. The simulations revealed that force generation ability (i.e. the force generated per unit of activation) of eight of the 11 muscles was significantly affected by walking or running speed. Soleus force generation ability decreased with increasing walking speed, but the transition from walking to running increased the force generation ability by reducing fiber velocities. Our results demonstrate the influence of soleus muscle architecture on the walk-to-run transition and the effects of muscle-tendon compliance on the plantarflexorsʼ ability to generate ankle moment and power. The study presents data that permit lower limb muscles to be studied in unprecedented detail by relating muscle fiber dynamics and force generation to the mechanical demands of walking and running.Supplementary material available online at

Foot placement has long been recognized as the primary mechanism that humans use to restore balance. Many biomechanists have examined where humans place their feet during gait, perturbations, and athletic events. Roboticists have also used foot placement as a means of control but with limited success. Recently, Wight et al. (2008, "Introduction of the Foot Placement Estimator: A Dynamic Measure of Balance for Bipedal Robotics," ASME J. Comput. Nonlinear Dyn., 3, p. 011009) introduced a planar foot placement estimator (FPE) algorithm that will restore balance to a simplified biped that is falling. This study tested the FPE as a candidate function for sagittal plane human-foot-placement (HFP) by recording the kinematics of 14 healthy subjects while they performed ten walking trials at three speeds. The FPE was highly correlated with HFP (rho>or=0.997) and its accuracy varied linearly from 2.6 cm to -8.3 cm as walking speed increased. A sensitivity analysis revealed that assumption violations of the FPE cannot account for the velocity-dependent changes in FPE-HFP error suggesting that this behavior is volitional.

Humans use carefully chosen step locations to restore their balance during locomotion and in response to perturbations. Understanding the relationship between foot placement and balance restoration is key to developing useful dynamic human balance diagnostic tests and balance rehabilitation treatments. The link between foot placement and balance restoration is studied in this paper using a simplified monopedal model that has a circular foot, coined the Euler pendulum. The Euler pendulum provides a convenient method of studying the stability properties of three-dimensional (3D) bipedal systems without the burden of large system equations typical of multibody systems. The Euler pendulum has unstable regions of its state-space that can be made to transition to a statically stable region using an appropriate foot placement location prior to contacting the ground. The planar foot placement estimator (FPE) method developed by Wight et al. is extended in this work in order to find foot placement locations in 3D to balance the 3D Euler pendulum. Preliminary experimental data shows that the 3D foot placement estimator (3DFPE) location corresponds very well with human foot placement during walking, gait termination, and when landing from a jump. In addition, a sensitivity analysis revealed that the assumptions of the 3DFPE are reasonable for human movement. Metrics for bipedal instability and balance performance suggested in this work could be of practical significance for health care professionals.