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Antoine Falisse, Sam Van Rossom, Johannes Gijsbers, Frans Steenbrink, Ben J.H. van Basten, Ilse Jonkers, Antonie J. van den Bogert and Friedl De Groote

. Different software systems were developed for modeling and analyzing human movement (eg, AnyBody, 1 OpenSim, 2 and Human Body Model 3 ), and there is an increasingly large body of literature reporting analyses of motion based on these software systems. OpenSim offers several musculoskeletal models with

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Stefan Sebastian Tomescu, Ryan Bakker, Tyson A.C. Beach and Naveen Chandrashekar

position and force platform data should be filtered with matched frequency filters to maintain consistency in the equations of motion when conducting inverse dynamics analyses. OpenSim (version 3.1; Stanford University, Stanford, CA) is a widely used musculoskeletal modeling program. 8 In addition to

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Sarah A. Roelker, Elena J. Caruthers, Rachel K. Hall, Nicholas C. Pelz, Ajit M.W. Chaudhari and Robert A. Siston

such as fine-wire electromyography (EMG) and tendon buckle transducers. OpenSim 1 is an open-source musculoskeletal modeling and simulation software employed by over 50,000 unique users 2 to investigate dynamic movements such as walking, 3 – 5 running, 6 , 7 rising from a chair, 8 and climbing

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Zachary F. Lerner, Derek J. Haight, Matthew S. DeMers, Wayne J. Board and Raymond C. Browning

Net muscle moments (NMMs) have been used as proxy measures of joint loading, but musculoskeletal models can estimate contact forces within joints. The purpose of this study was to use a musculoskeletal model to estimate tibiofemoral forces and to examine the relationship between NMMs and tibiofemoral forces across walking speeds. We collected kinematic, kinetic, and electromyographic data as ten adult participants walked on a dual-belt force-measuring treadmill at 0.75, 1.25, and 1.50 m/s. We scaled a musculoskeletal model to each participant and used OpenSim to calculate the NMMs and muscle forces through inverse dynamics and weighted static optimization, respectively. We determined tibiofemoral forces from the vector sum of intersegmental and muscle forces crossing the knee. Estimated tibiofemoral forces increased with walking speed. Peak earlystance compressive tibiofemoral forces increased 52% as walking speed increased from 0.75 to 1.50 m/s, whereas peak knee extension NMMs increased by 168%. During late stance, peak compressive tibiofemoral forces increased by 18% as speed increased. Although compressive loads at the knee did not increase in direct proportion to NMMs, faster walking resulted in greater compressive forces during weight acceptance and increased compressive and anterior/posterior tibiofemoral loading rates in addition to a greater abduction NMM.

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Hans Kainz, Hoa X. Hoang, Chris Stockton, Roslyn R. Boyd, David G. Lloyd and Christopher P. Carty

participants, and/or patients with bone deformity. 12 The OpenSim best practice documentation recommends that users use markers that correspond to anatomical landmarks and joint centers to scale the generic model. 13 Several studies compared scaled with medical imaging based models 9 , 14

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Elena J. Caruthers, Julie A. Thompson, Ajit M.W. Chaudhari, Laura C. Schmitt, Thomas M. Best, Katherine R. Saul and Robert A. Siston

Sit-to-stand transfer is a common task that is challenging for older adults and others with musculoskeletal impairments. Associated joint torques and muscle activations have been analyzed two-dimensionally, neglecting possible three-dimensional (3D) compensatory movements in those who struggle with sit-to-stand transfer. Furthermore, how muscles accelerate an individual up and off the chair remains unclear; such knowledge could inform rehabilitation strategies. We examined muscle forces, muscleinduced accelerations, and interlimb muscle force differences during sit-to-stand transfer in young, healthy adults. Dynamic simulations were created using a custom 3D musculoskeletal model; static optimization and induced acceleration analysis were used to determine muscle forces and their induced accelerations, respectively. The gluteus maximus generated the largest force (2009.07 ± 277.31 N) and was a main contributor to forward acceleration of the center of mass (COM) (0.62 ± 0.18 m/s2), while the quadriceps opposed it. The soleus was a main contributor to upward (2.56 ± 0.74 m/s2) and forward acceleration of the COM (0.62 ± 0.33 m/s2). Interlimb muscle force differences were observed, demonstrating lower limb symmetry cannot be assumed during this task, even in healthy adults. These findings establish a baseline from which deficits and compensatory strategies in relevant populations (eg, elderly, osteoarthritis) can be identified.

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Amy R. Lewis, William S.P. Robertson, Elissa J. Phillips, Paul N. Grimshaw and Marc Portus

(depicting the physics of the athlete–wheelchair system) and the limited capacity for measuring all of these. Few musculoskeletal models within OpenSim are designed to represent individuals with physical impairments, with none explicitly available for wheelchair racing. Within the limited musculoskeletal

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Ming Xiao and Jill Higginson

Generic muscle parameters are often used in muscle-driven simulations of human movement to estimate individual muscle forces and function. The results may not be valid since muscle properties vary from subject to subject. This study investigated the effect of using generic muscle parameters in a muscle-driven forward simulation on muscle force estimation. We generated a normal walking simulation in OpenSim and examined the sensitivity of individual muscle forces to perturbations in muscle parameters, including the number of muscles, maximum isometric force, optimal fiber length, and tendon slack length. We found that when changing the number of muscles included in the model, only magnitude of the estimated muscle forces was affected. Our results also suggest it is especially important to use accurate values of tendon slack length and optimal fiber length for ankle plantar flexors and knee extensors. Changes in force production by one muscle were typically compensated for by changes in force production by muscles in the same functional muscle group, or the antagonistic muscle group. Conclusions regarding muscle function based on simulations with generic musculoskeletal parameters should be interpreted with caution.

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R. Tyler Richardson, Elizabeth A. Rapp, R. Garry Quinton, Kristen F. Nicholson, Brian A. Knarr, Stephanie A. Russo, Jill S. Higginson and James G. Richards

sequences for abduction and forward reach, respectively, as only the first rotation was of interest for this study. All simulations were performed on the MoBL ARMS dynamic musculoskeletal model of the upper extremity 7 using OpenSim 3.3. 1 The MoBL ARMS model’s unscaled segment lengths and

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Christopher M. Saliba, Allison L. Clouthier, Scott C.E. Brandon, Michael J. Rainbow and Kevin J. Deluzio

musculoskeletal model and processing paradigm. 23 In brief, radiographic measurements were used to scale the model and set the frontal plane knee alignment and contact locations. Inverse kinematics, inverse dynamics, and muscle moment arms about the hip, knee, and ankle were computed in OpenSim 24 (Stanford