OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement

Abstract

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.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1. Elements of a typical musculoskeletal simulation in OpenSim.

Movement arises from a complex orchestration of the neural, muscular, skeletal, and sensory systems. OpenSim includes computational models of these systems, enabling prediction and analysis of human and animal movement. Neural command to muscles, in the form of excitations, can be estimated from controller models or experimental data (e.g., EMG). OpenSim’s Hill-type musculotendon models, which translate excitations into muscle forces, include the force–length and force–velocity properties of muscles. OpenSim provides the flexibility to represent the wide range of muscle geometry found in animals, and the parameters defining muscle geometry and contraction dynamics can be modified based on experimental data. OpenSim’s underlying Simbody engine for multibody dynamics includes contact models (e.g., to simulate foot–ground interaction) and several solvers/integrators that allow users to generate muscle-driven simulations (forward simulation) or to solve for muscle forces and moments that generate an observed motion (inverse simulation).

Fig 2. The OpenSim community is worldwide, diverse, and growing.

(A) Locations of visitors to the OpenSim documentation (sessions per country in the 1-year period ending April 21, 2018). Since its launch in 2012, the OpenSim documentation wiki has been visited by over 25,000 users from around the world per year [30]. (B) Publications citing OpenSim by research category (Web of Science). Note that journals, and thus citations of OpenSim, may belong to more than one research category. According to Google Scholar, OpenSim [27] has been cited 1947 times as of June 13, 2018; based on analysis of the subset of these papers published in 2016, we estimate that 3/4 of these publications make use of the software. (C) Cumulative downloads of OpenSim since its release in August 2007. 35,915 users have downloaded the software as of June 13, 2018 [31]. World map in (A) created using tools at http://gunn.co.nz/map.

Fig 3. The OpenSim desktop application.

A graphical user interface provides access to tools for inspecting, modifying, and simulating musculoskeletal models. Shown here are the results of muscle-driven simulations of human and chimpanzee walking that were generated by tracking experimental motion capture data. OpenSim models can be augmented with passive and active devices to explore designs of exoskeletons. (Human model and simulation from Rajagopal et al. [34]; chimpanzee model from O’Neill et al. [35] and unpublished simulation results provided by M.C. O’Neill and B.R. Umberger.)

Fig 4. The OpenSim framework is used to study the dynamics of human and animal musculoskeletal systems.

An OpenSim Model is a codified description of a physical system and its dynamics, and can be expressed as a topological graph of interconnected components. Each component represents a self-contained module (biological structure, neuromotor controller, mechatronic device, etc.) comprising the Model, and contributes to building the computational system. The computational system consists of two parts: (1) the system of equations (“System”), which includes physical parameters that are constant during a simulation (mass, dimensions, muscle properties, etc.); and (2) the State, which is the list of all variables in the System that may vary over time (e.g., joint angles). The model developer designs an OpenSim Model that represents the physical system of interest, and the OpenSim software automatically constructs the computational system of differential and algebraic equations that describe the dynamics of the Model.

Fig 5. A variety of experimental and simulated data are used to validate OpenSim models.

For example, our models of muscle contraction dynamics [46] were validated using in vivo isolated rat soleus muscle data from Krylow and Sandercock [51]. The data shown here (second column) were collected from one of these sources (force transducer; first column) as the muscle was maximally excited and its free end was displaced according to a predetermined time-varying signal, repeating for various displacements (shown here for 0.10–1.00 mm). We replicated these experiments in simulation to validate our computational model of muscle contraction dynamics [46].

Fig 6. OpenSim enables physically accurate simulation of neuromusculoskeletal systems.

Physics-based models of biological structures can be augmented with models of neuromotor controllers and mechatronic devices to reproduce and explain experimental observations, and to predict novel movements. OpenSim natively supports a wide variety of components, including those for modeling the skeleton as rigid bodies connected by joints, ligaments and other passive structures, muscles and motors, tracking and reflex-based controllers, external forces from the environment, and assistive devices composed of rigid bodies, joints, springs, and actuators. We have added new components to OpenSim (indicated with “†”) and enhanced many existing components (indicated with “*”). OpenSim’s collaborative, open-source development philosophy allows users to create, extend, and share new component models to accelerate their research.

Fig 7. OpenSim facilitates defining anatomically accurate musculoskeletal models to reveal relationships between form and function.

In this study, Rankin et al. [12] built a detailed model of an ostrich (Struthio camelus) pelvic limb in OpenSim (A) and collected motion capture data to generate simulations of ostrich locomotion. The researchers generated simulations of running (navy blue) and walking (light blue) with compliant tendons, using the Computed Muscle Control Tool in OpenSim. For each muscle group, they computed the negative and positive work performed by the muscles during stance (B) and swing (not shown). The biarticular muscles crossing both the hip and knee performed largely positive work during stance, contributing to propulsion, while the knee extensors performed negative work, acting as brakes. Adapted from Rankin et al. [12].

Fig 8. OpenSim supports design and analysis of implantable devices to restore grasp for those with paralysis.

In current practice, individuals with partial paralysis of the upper limb receive tendon transfer surgeries to reconnect the tendons that facilitate finger movement to a non-paralyzed donor muscle. Homayouni and colleagues [14] are designing implantable devices to improve outcomes of traditional, suture-based tendon transfer. In one design (A), the single-suture attachment is replaced with an artificial tendon network. In a second design (B), a lever mechanism replaces the suture to more evenly distribute forces between the digits. The investigators used OpenSim to model the traditional suture-based surgery and each proposed design, and simulated a grasping motion. The implantable devices (green and blue) achieved greater finger flexion (C) than the traditional suture-based surgery (black). Adapted from Homayouni et al. [14].

Fig 9. OpenSim reveals the roles of reflexes and preparatory co-activation in preventing ankle injury.

DeMers and colleagues [20] created an OpenSim model to study risky landing scenarios (A), which included a detailed ankle joint to model both passive and active components, stretch-reflex controllers to actuate the muscles, and a contact model to estimate foot–floor reaction forces. They used the model to simulate a single-leg drop-landing onto an angled surface, which induced rapid ankle inversion. While preparatory co-activation of the invertor and evertor muscles (B) was able to prevent the ankle from inverting to angles that may cause injury (gray region), a reflex-only strategy (C) was not able to prevent injury in the scenario studied. Models and data are available on simtk.org [23]. Adapted from DeMers et al. [20].

Fig 10. Combining neural and musculoskeletal models to study neuromodulation of spinal circuits for correcting motor deficits.

Moraud et al. [78] measured the movement of spinal cord–injured rats (left panel; experimental setup with marker kinematics, ground reaction forces, and muscle electromyography (EMG)). A musculoskeletal model of the rat hindlimb (center panel) was developed in OpenSim to provide estimates of muscle fiber lengths and velocities from measured kinematics, which were inputs to muscle spindle models (right panel; black coils). Spindle reflexes from the major flexor and extensor muscles were the primary inputs to a realistic model of spinal neuronal circuits (right panel), which generated the neural drive to the same major muscle groups. The spindle reflexes were coupled to electrical epidural stimulation (EES) that modulated the spindle signals into the spinal circuits. The results (not shown) from Moraud et al. revealed that simulated neuromuscular activity successfully predicted changes of in vivo muscle activity (EMG) due to variations in EES frequency and amplitude. The OpenSim model of the rat hindlimb by Johnson et al. is available on simtk.org [90].