Scott L. Delp

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

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.

An interactive graphics-based model of the lower extremity to study orthopaedic surgical procedures

A model is developed of the human lower extremity to study how changes in musculoskeletal geometry and musculotendon parameters affect muscle force and its moment about the joints. The lines of action of 43 musculotendon actuators were defined based on their anatomical relationships to three-dimensional bone surface representations. A model for each actuator was formulated to compute its isometric force-length relation. The kinematics of the lower extremity were defined by modeling the hip, knee, ankle, subtalar, and metatarsophalangeal joints. Thus, the force and joint moment that each musculotendon actuator develops can be computed for any body position. The joint moments calculated with the model compare well with experimentally measured isometric joint moments. A graphical interface to the model has also been developed. It allows the user to visualize the musculoskeletal geometry and to manipulate the model parameters to study the biomechanical consequences of orthopaedic surgical procedures. For example, tendon transfer and lengthening procedures can be simulated by adjusting the model parameters according to various surgical techniques. Results of the simulated surgeries can be analyzed quickly in terms of postsurgery muscle forces and other biomechanical variables.<<ETX>>

A model of the upper extremity for simulating musculoskeletal surgery and analyzing neuromuscular control.

Biomechanical models of the musculoskeletal system are frequently used to study neuromuscular control and simulate surgical procedures. To be broadly applicable, a model must be accessible to users, provide accurate representations of muscles and joints, and capture important interactions between joints. We have developed a model of the upper extremity that includes 15 degrees of freedom representing the shoulder, elbow, forearm, wrist, thumb, and index finger, and 50 muscle compartments crossing these joints. The kinematics of each joint and the force-generating parameters for each muscle were derived from experimental data. The model estimates the muscle–tendon lengths and moment arms for each of the muscles over a wide range of postures. Given a pattern of muscle activations, the model also estimates muscle forces and joint moments. The moment arms and maximum moment-generating capacity of each muscle group (e.g., elbow flexors) were compared to experimental data to assess the accuracy of the model. These comparisons showed that moment arms and joint moments estimated using the model captured important features of upper extremity geometry and mechanics. The model also revealed coupling between joints, such as increased passive finger flexion moment with wrist extension. The computer model is available to researchers at http://nmbl.stanford.edu.

A graphics-based software system to develop and analyze models of musculoskeletal structures

We have created a graphics-based software system that enables users to develop and analyze musculoskeletal models without programming. To define a model using this system one specifies the surfaces of the bones, the kinematics of the joints and the lines of action and force-generating parameters of the muscles. Once a model is defined, the function of each muscle can be analyzed by computing its length, moment arms, force and joint moments. The software has been implemented on a computer graphics workstation so that users can view the model from any perspective and graphically manipulate the joint kinematics and musculoskeletal geometry. Models can also be animated to visualize the results of motion analysis experiments. Since the software can be used to study models of many different musculoskeletal structures, it can enhance the productivity of investigators working on diverse problems in biomechanics.

A Model of the Lower Limb for Analysis of Human Movement

Computer models that estimate the force generation capacity of lower limb muscles have become widely used to simulate the effects of musculoskeletal surgeries and create dynamic simulations of movement. Previous lower limb models are based on severely limited data describing limb muscle architecture (i.e., muscle fiber lengths, pennation angles, and physiological cross-sectional areas). Here, we describe a new model of the lower limb based on data that quantifies the muscle architecture of 21 cadavers. The model includes geometric representations of the bones, kinematic descriptions of the joints, and Hill-type models of 44 muscle–tendon compartments. The model allows calculation of muscle–tendon lengths and moment arms over a wide range of body positions. The model also allows detailed examination of the force and moment generation capacities of muscles about the ankle, knee, and hip and is freely available at www.simtk.org.

Generating dynamic simulations of movement using computed muscle control

Computation of muscle excitation patterns that produce coordinated movements of muscle-actuated dynamic models is an important and challenging problem. Using dynamic optimization to compute excitation patterns comes at a large computational cost, which has limited the use of muscle-actuated simulations. This paper introduces a new algorithm, which we call computed muscle control, that uses static optimization along with feedforward and feedback controls to drive the kinematic trajectory of a musculoskeletal model toward a set of desired kinematics. We illustrate the algorithm by computing a set of muscle excitations that drive a 30-muscle, 3-degree-of-freedom model of pedaling to track measured pedaling kinematics and forces. Only 10 min of computer time were required to compute muscle excitations that reproduced the measured pedaling dynamics, which is over two orders of magnitude faster than conventional dynamic optimization techniques. Simulated kinematics were within 1 degrees of experimental values, simulated pedal forces were within one standard deviation of measured pedal forces for nearly all of the crank cycle, and computed muscle excitations were similar in timing to measured electromyographic patterns. The speed and accuracy of this new algorithm improves the feasibility of using detailed musculoskeletal models to simulate and analyze movement.

Influence of muscle morphometry and moment arms on the moment-generating capacity of human neck muscles

Study Design. The function of neck muscles was quantified by incorporating experimentally measured morphometric parameters into a three‐dimensional biomechanical model. Objective. To analyze how muscle morphometry and moment arms influence moment‐generating capacity of human neck muscles in physiologic ranges of motion. Summary of Background Data. Previous biomechanical analyses of the head‐neck system have used simplified representations of the musculoskeletal anatomy. The force‐ and moment‐generating properties of individual neck muscles have not been reported. Methods. A computer graphics model was developed that incorporates detailed neck muscle morphometric data into a model of cervical musculoskeletal anatomy and intervertebral kinematics. Moment arms and force‐generating capacity of neck muscles were calculated for a range of head positions. Results. With the head in the upright neutral position, the muscles with the largest moment arms and moment‐generating capacities are sternocleidomastoid in flexion and lateral bending, semispinalis capitis and splenius capitis in extension, and trapezius in axial rotation. The moment arms of certain neck muscles (e.g., rectus capitis posterior major in axial rotation) change considerably in the physiologic range of motion. Most neck muscles maintain at least 80% of their peak force‐generating capacity throughout the range of motion; however, the force‐generating capacities of muscles with large moment arms and/or short fascicles (e.g., splenius capitis) vary substantially with head posture. Conclusions. These results quantify the contributions of individual neck muscles to moment‐generating capacity and demonstrate that variations in force‐generating capacity and moment arm throughout the range of motion can alter muscle moment‐generating capacities.

Computer assisted knee replacement.

Accurate alignment of knee implants is essential for the success of total knee replacement. Although mechanical alignment guides have been designed to improve alignment accuracy, there are several fundamental limitations of this technology that will inhibit additional improvements. Various computer assisted techniques have been developed to examine the potential to install knee implants more accurately and consistently than can be done with mechanical guides. For example, computer integrated instrumentation incorporates highly accurate measurement devices to locate joint centers, track surgical tools, and align prosthetic components. Image guided knee replacement provides a three-dimensional preoperative plan that guides the placement of the cutting blocks and prosthetic components. Robot assisted knee replacement allows one to machine bones accurately without the use of standard cutting blocks. The rationale for the development of computer assisted knee replacement systems is presented, the operation of several different systems is described, the advantages and disadvantages of different approaches are discussed, and areas for future research are suggested.

Variation of muscle moment arms with elbow and forearm position

We hypothesized that the moment arms of muscles crossing the elbow vary substantially with forearm and elbow position and that these variations could be represented using a three-dimensional computer model. Flexion/extension and pronation/supination moment arms of the brachioradialis, biceps, brachialis, pronator teres, and triceps were calculated from measurements of tendon displacement and joint angle in two anatomic specimens and were estimated using a computer model of the elbow joint. The anatomical measurements revealed that the flexion/extension moment arms varied by at least 30% over a 95 degrees range of motion. The changes in flexion/extension moment arm magnitudes with elbow flexion angle were represented well by the computer model. The anatomical studies and the computer model demonstrate that the biceps flexion moment arm peaks in a more extended elbow position and has a larger peak when the forearm is supinated. Also, the peak biceps supination moment arm decreases as the elbow is extended. These results emphasize the need to account for the variation of muscle moment arms with elbow flexion and forearm position.

Muscle contributions to propulsion and support during running

Muscles actuate running by developing forces that propel the body forward while supporting the bodys weight. To understand how muscles contribute to propulsion (i.e., forward acceleration of the mass center) and support (i.e., upward acceleration of the mass center) during running we developed a three-dimensional muscle-actuated simulation of the running gait cycle. The simulation is driven by 92 musculotendon actuators of the lower extremities and torso and includes the dynamics of arm motion. We analyzed the simulation to determine how each muscle contributed to the acceleration of the body mass center. During the early part of the stance phase, the quadriceps muscle group was the largest contributor to braking (i.e., backward acceleration of the mass center) and support. During the second half of the stance phase, the soleus and gastrocnemius muscles were the greatest contributors to propulsion and support. The arms did not contribute substantially to either propulsion or support, generating less than 1% of the peak mass center acceleration. However, the arms effectively counterbalanced the vertical angular momentum of the lower extremities. Our analysis reveals that the quadriceps and plantarflexors are the major contributors to acceleration of the body mass center during running.