The use of demonstrations, or modeling, is among the most commonly used instructional strategies, and the idea that one could learn a motor skill by watching another perform it has intrigued scholars for decades (for recent reviews, see Ong & Hodges, 2012 ; Rosen, Salas, Pavlas, Jensen, Fu
Paul J. Felton, Maurice R. Yeadon, and Mark A. King
In recent years, 3-dimensional (3D) forward dynamics muscle-driven models have increasingly been employed to analyze human movement. However, due to the difficulty associated with obtaining accurate subject-specific muscle model parameters, most subject-specific 3D muscle-driven models typically
efficacy of eccentric cycling has attracted researchers to adopt eccentric cycling as a model for exercise interventions. Although the efficacy of eccentric cycling has been proved, some potential barriers prevented the eccentric cycling becoming popular, such as limited number of commercially available
Eva Piatrikova, Nicholas J. Willsmer, Marco Altini, Mladen Jovanović, Lachlan J.G. Mitchell, Javier T. Gonzalez, Ana C. Sousa, and Sean Williams
athletes 4 , 9 in various sports, including swimming. Consequently, HRV has become a promising candidate for monitoring global responses of athletes to training. 1 – 4 Given this, Chalencon et al 6 explored the possibility of applying the Banister impulse-response (IR) model 10 to describe the impact
Gilles Dietrich, Jonathan Bredin, and Yves Kerlirzin
The aim of this article is to elaborate a general framework for modeling dual opposition activities, or more generally, dual interaction. The main hypothesis is that opposition behavior can be measured directly from a global variable and that the relative distance between the two subjects can be this parameter. Moreover, this parameter should be considered as multidimensional parameter depending not only on the dynamics of the subjects but also on the “internal” parameters of the subjects, such as sociological and/or emotional states. Standard and simple mechanical formalization will be used to model this multifactorial distance. To illustrate such a general modeling methodology, this model was compared with actual data from an opposition activity like Japanese fencing (kendo). This model captures not only coupled coordination, but more generally interaction in twosubject activities.
Daniel Cury Ribeiro, Joelly Mahnic de Toledo, Roberto Costa Krug, and Jefferson Fagundes Loss
Shoulder injuries are often related to rotator cuff muscles. Although there are various models for muscle force estimation, it is difficult to ensure that the results obtained with such models are reliable. The aim of the current study was to compare two models of muscle force estimation. Eight subjects, seven male and one female (mean age of 24 yr; mean height of 1.83 m), performed five isokinetic maximum concentric contractions of internal and external shoulder rotation. Two models with different algorithms were used. In both, the input data consisted of the measured internal rotation moment. Comparisons were made between the difference and the average results obtained with each model of muscle force estimation. There was reasonable agreement among the results for force between the two models for subscapularis, pectoralis major, and anterior deltoideus muscles results. Conversely, poor correlation was found for the latissimus dorsi, teres major, and middle deltoid. These results suggest that the algorithm structure might have a strong effect on muscle force estimation results.
Jonathan R. Kusins, Ryan Willing, Graham J.W. King, and Louis M. Ferreira
A computational elbow joint model was developed with a main goal of providing complimentary data to experimental results. The computational model was developed and validated using an experimental elbow joint phantom consisting of a linked total joint replacement. An established in-vitro motion simulator was used to actively flex/extend the experimental elbow in multiple orientations. Muscle forces predicted by the computational model were similar to the experimental model in 4 out of the 5 orientations with errors less than 7.5 N. Valgus angle kinematics were in agreement with differences less than 2.3°. In addition, changes in radial head length, a clinically relevant condition following elbow reconstruction, were simulated in both models and compared. Both lengthening and shortening of the radial head prosthesis altered muscle forces by less than 3.5 N in both models, and valgus angles agreed within 1°. The computational model proved valuable in cross validation with the experimental model, elucidating important limitations in the in-vitro motion simulator’s controller. With continued development, the computational model can be a complimentary tool to experimental studies by providing additional noninvasive outcome measurements.
Jason Wicke and Genevieve A. Dumas
The geometric method combines a volume and a density function to estimate body segment parameters and has the best opportunity for developing the most accurate models. In the trunk, there are many different tissues that greatly differ in density (e.g., bone versus lung). Thus, the density function for the trunk must be particularly sensitive to capture this diversity, such that accurate inertial estimates are possible. Three different models were used to test this hypothesis by estimating trunk inertial parameters of 25 female and 24 male college-aged participants. The outcome of this study indicates that the inertial estimates for the upper and lower trunk are most sensitive to the volume function and not very sensitive to the density function. Although it appears that the uniform density function has a greater influence on inertial estimates in the lower trunk region than in the upper trunk region, this is likely due to the (overestimated) density value used. When geometric models are used to estimate body segment parameters, care must be taken in choosing a model that can accurately estimate segment volumes. Researchers wanting to develop accurate geometric models should focus on the volume function, especially in unique populations (e.g., pregnant or obese individuals).
Yu-Jen Chen, Irving Scher, and Christopher M. Powers
The purpose of this study was to describe an imaging based, subject specific model that was developed to quantify patellofemoral joint reaction forces (PFJRF’s). The secondary purpose was to test the model in a group of healthy individuals while performing various functional tasks. Twenty healthy subjects (10 males, 10 females) were recruited. All participants underwent two phases of data collection: 1) magnetic resonance imaging of the knee, patellofemoral joint, and thigh, and 2) kinematic, kinetic and EMG analysis during walking, running, stair ascent, and stair descent. Using data obtained from MRI, a subject specific representation of the extensor mechanism was created. Individual gait data were used to drive the model (via an optimization routine) and three-dimensional vasti muscle forces and subsequent three-dimensional PFJRF’s were computed. The average peak PFJRF was found to be highest during running (58.2 N/kg-bwt), followed by stair ascent (33.9 N/kg-bwt), stair descent (27.9 N/kg-bwt), and walking (10.1 N/kg-bwt). No differences were found between males and females. For all conditions, the direction of the PFJRF was always in the posterior, superior, and lateral directions. The posterior component of the PFJRF always had the greatest magnitude, followed by superior and lateral components. Our results indicate that estimates of the magnitude and direction of the PFJRF during functional tasks can be obtained using a 3D-imaging based model.
Rahman Davoodi and Gerald E. Loeb
Computer models of the neuromusculoskeletal systems can be used to study different aspects of movement and its control in humans and animals. SIMM with Dynamics Pipeline (Musculographics Inc., Chicago) and SD-Fast (Symbolic Dynamics Inc., Mountain View, CA) are software packages commonly used for graphic and dynamic simulation of movement in musculoskeletal systems. Building dynamic models with SIMM requires substantial C programming, however, which limits its use. We have developed Musculoskeletal Modeling in Simulink (MMS) software to convert the SIMM musculoskeletal and kinetics models to Simulink (Mathworks Inc., Natick, MA) blocks. In addition, MMS removes SIMM’s run-time constraints so that the resulting blocks can be used in simulations of closed-loop sensorimotor control systems.