Craig M. Goehler

Benchmarking of dynamic simulation predictions in two software platforms using an upper limb musculoskeletal model

Several opensource or commercially available software platforms are widely used to develop dynamic simulations of movement. While computational approaches are conceptually similar across platforms, technical differences in implementation may influence output. We present a new upper limb dynamic model as a tool to evaluate potential differences in predictive behavior between platforms. We evaluated to what extent differences in technical implementations in popular simulation software environments result in differences in kinematic predictions for single and multijoint movements using EMG- and optimization-based approaches for deriving control signals. We illustrate the benchmarking comparison using SIMM–Dynamics Pipeline–SD/Fast and OpenSim platforms. The most substantial divergence results from differences in muscle model and actuator paths. This model is a valuable resource and is available for download by other researchers. The model, data, and simulation results presented here can be used by future researchers to benchmark other software platforms and software upgrades for these two platforms.

The sensitivity of endpoint forces produced by the extrinsic muscles of the thumb to posture

This study utilizes a biomechanical model of the thumb to estimate the force produced at the thumb-tip by each of the four extrinsic muscles. We used the principle of virtual work to relate joint torques produced by a given muscle force to the resulting endpoint force and compared the results to two separate cadaveric studies. When we calculated thumb-tip forces using the muscle forces and thumb postures described in the experimental studies, we observed large errors. When relatively small deviations from experimentally reported thumb joint angles were allowed, errors in force direction decreased substantially. For example, when thumb posture was constrained to fall within +/-15 degrees of reported joint angles, simulated force directions fell within experimental variability in the proximal-palmar plane for all four muscles. Increasing the solution space from +/-1 degrees to an unbounded space produced a sigmoidal decrease in error in force direction. Changes in thumb posture remained consistent with a lateral pinch posture, and were generally consistent with each muscles function. Altering thumb posture alters both the components of the Jacobian and muscle moment arms in a nonlinear fashion, yielding a nonlinear change in thumb-tip force relative to muscle force. These results explain experimental data that suggest endpoint force is a nonlinear function of muscle force for the thumb, support the continued use of methods that implement linear transformations between muscle force and thumb-tip force for a specific posture, and suggest the feasibility of accurate prediction of lateral pinch force in situations where joint angles can be measured accurately.

The Effects of Shoe Architecture on Heel Impact Forces During Gait

In the current athletic footwear market, there exists a range of shoe architectures that offer a variety of support and flexibility options. The importance of footwear type has proved to be significant in the prevention of acute injuries due to impact forces [1, 2]. It has been shown that impact forces have most often been implicated in overuse running injuries, such as stress fractures and plantar fasciitis [2]. Additionally, material properties of damping elements, such as shoes, have demonstrated an effect on impact forces. Athletic footwear is categorized by the attribute of flexibility. The natural flex observed in the sole determines the flexibility; a more flexible shoe flexes closer to the mid-foot region, while a shoe designed for stability will flex closer to the ball of the shoe. Prior work has quantified the material stiffness of different shoe architectures with stability shoes possessing higher material stiffness than flexible shoes [3].Copyright

Estimating Individual Joint Contributions to Recorded Upper Extremity Movements Using OpenSim

A multitude of tasks are performed every day with minimal direct thought on how to orient the arm. While there have been many studies concerning upper extremity motion, there has been relatively little research reported in the area of how individual joint contributions vary between different upper extremity tasks. Such studies are necessary in order to accurately recreate dynamic motions of the arm using mechanical devices, e.g. prosthetic limbs. One difficulty in directly measuring these individual joint contributions in physical experiments is that most tasks are multi-joint movements and the limb segments influence each other causing passive interactive torques [1]. In order to quantify the individual joint contributions, it is beneficial to examine recorded arm movements within a simulation environment such as OpenSim [2].Copyright

Computational development of Jacobian matrices for complex spatial manipulators

Current methods for developing manipulator Jacobian matrices are based on traditional kinematic descriptions such as Denavit and Hartenberg parameters. The resulting symbolic equations for these matrices become cumbersome and computationally inefficient when dealing with more complex spatial manipulators, such as those seen in the field of biomechanics. This paper develops a modified method for Jacobian development based on generalized kinematic equations that incorporates partial derivatives of matrices with Leibnizs Law (the product rule). It is shown that a set of symbolic matrix functions can be derived that improve computational efficiency when used in MATLAB(®) M-Files and are applicable to any spatial manipulator. An articulated arm subassembly and a musculoskeletal model of the hand are used as examples.

Dynamic Simulation of Upper Limb Movement in Two Software Platforms

There are several opensource or commercially available software platforms widely used for the development of dynamic simulations of movement. While computational approaches to calculating the dynamics of a musculoskeletal model are conceptually similar across platforms, differences in implementation may influence simulation output. To understand predictions made using simulation, it is important to understand differences that may result from the choice of model or platform. Our aims were to 1) develop a musculoskeletal model of the upper limb suitable for dynamic simulation and 2) evaluate the influence of the choice between SIMM-SD/Fast and OpenSim simulation platforms on gravity- and EMG-driven simulations of movement.Copyright

The effects of shoe architecture on impact forces during gait: A continuation study

In the current athletic footwear market, there exists a wide range of shoe architectures that offer a variety of options in terms of flexibility and support. The importance of footwear type has proved to be significant in the prevention of an assortment of injuries, including knee osteoarthritis [1, 2]. Footwear type has also been shown to affect the lower extremity kinematics as well as the regulation of leg stiffness for a subject during dynamic activities [3]. An important attribute used to categorize athletic footwear architecture is the inherent flexibility of the shoe. The natural flex observed in the sole of the shoe determines the level of flexibility; a more flexible shoe will flex closer to the mid-foot region while a shoe designed for stability will flex closer to the ball of the shoe.Copyright

Should the neural–mechanical behaviour of a muscle be coupled or co‐vary?

muscle (FDI) of the hand. By using this particular muscle, the authors were able to circumvent many of the limitations traditionally associated with making the neural‐mechanical comparisons at joints controlled by many muscles. The results from that study led the authors to conclude that the neural drive to the FDI muscle and the mechanical advantage of the muscle are coupled when the thumb changes position. The remainder of this note will focus on the impact of the study and the interpretation of the results, specifically with regard to whether the neural drive to the muscle and the muscle mechanics should co-vary or be coupled when performing motor tasks. The study by Hudson et al. (2009) recorded end-point forces produced (i) by ulnar nerve stimulation and (ii) during static index finger flexion efforts, with the thumb positioned in a number of ‘thumb up’ and ‘thumb down’ postures. Although a seemingly minor postural adjustment, ulnar nerve stimulation resulted in a 60% larger end-point twitch force in the thumb down position compared to thumb up. Furthermore, ultrasound results indicated that, relative to thumb up, the thumb down posture caused a 65% increase in the distance between the FDI tendon and the lateral tubercle of the second metacarpal, which was used to estimate FDI moment arm magnitude. Thus, the mechanical advantage for FDI force transmission across the metacarpophalangeal (MCP) joint was significantly increased with the thumb down, regardless of muscle force. Based on the mechanics, end-point force is directly proportional to the MCP joint torque, via the geometric configuration of the system. In other words, sincetheindexfingerwassplintedandMCP jointangleremainedconstant,momentarm magnitude was the main geometric factor determining how effectively FDI muscle force was transformed into end-point force. The end-point forces generated by supramaximal ulnar nerve stimulation (i.e. constant neural input) illustrate the proportionality between MCP joint torque and end-point force (a 65% increase in moment arm → 60% increase in end-point force). However, the mechanics are only one aspect of generating joint torques. Can, or does, the central nervous system

The Effects of Joint Posture and Segment Lengths on the Thumb-Tip Force Produced by Flexor Pollicis Longus

The ability to hold an object between the thumb and the lateral aspect of the index finger (lateral pinch) is an important aspect of hand function. Previous studies suggest that current biomechanical models of the thumb do not accurately predict lateral pinch force [1, 2]. These studies modeled the thumb using simplified joint descriptions based on orthogonal and intersecting axes of rotation. A detailed anatomical study indicates that the axes of rotation of the thumb are actually both non-intersecting and non-orthogonal [3]. It has also been reported that the anatomical variability observed in the data support four different representations of thumb kinematics, all with non-intersecting and non-orthogonal axes [4].Copyright