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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

and for the marker weights used to fit the model’s pose to the standing calibration pose, respectively) and to initialize a new model in Human Body Model. 3 The processing pipeline with both systems consisted of inverse kinematics, kinematic filtering, inverse dynamics, and static optimization. The

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Thomas W. Kernozek, Naghmeh Gheidi, Matthew Zellmer, Jordan Hove, Becky L. Heinert and Michael R. Torry

moments by minimizing a static cost function where the sum of squared muscle activations was related to maximum muscle strength. 34 , 36 A recurrent neural network was used to solve the static optimization problem. 37 Total quadriceps force (QF) was obtained by summing the muscle forces of the rectus

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Abbigail Ristow, Matthew Besch, Drew Rutherford and Thomas W. Kernozek

this hopping distance. Human Body Model Software (Motekforce Link, Amsterdam, The Netherlands) was used to calculate joint kinetics, kinematics, and muscle forces. These output data were used in a custom PFJ model to calculate PFJS. 32 Inverse dynamics and static optimization were used to calculate

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Steven A. Kautz, Richard R. Neptune and Felix E. Zajac

The target article presents a framework for coordination of one- and two-joint muscles in a variety of tasks. Static optimization analyses were performed that minimize muscle fatigue, and it is claimed that the predicted muscle forces account for essential features of EMG activity “qualitatively” well. However, static optimization analyses use the observed joint moments, which implicitly assumes that they minimize the total muscle fatigue of the task. We use a forward dynamics (i.e., relationship between muscle forces and the kinematics and kinetics of task performance) modeling approach to show that this assumption does not appear to be true in cycling (which was used as an example task in the target article). Our results challenge the hypothesized coordination framework and the underlying concept that general coordination principles for dynamic tasks can be elucidated using inverse-dynamics-based analyses.

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Christina Olbrantz, Jamie Bergelin, Jill Asmus, Thomas Kernozek, Drew Rutherford and Naghmeh Gheidi

Patellofemoral pain (PFP) is common in females. Patellofemoral joint stress (PFJS) may be important in the development of PFP. Ground reaction force (GRF) during landing activities may impact PFJS. Our purpose was to determine how healthy females alter their landing mechanics using visual posttrial feedback on their GRF and assess how PFJS changes. Seventeen participants performed a series of drop landings during 3 conditions: baseline, feedback, and postfatigue feedback. The fatigue protocol used repetitive jump squats. Quadriceps force was estimated through inverse-dynamics-based static optimization approach. Then, PFJS was calculated using a musculoskeletal model. Multivariate differences were shown across conditions (P = .01). Univariate tests revealed differences in PFJS (P = .014), knee range of motion (P = .001), and GRF (P = .005). There were no differences in quadriceps force (P = .125). PFJS and GRF decreased from baseline to feedback (P = .002, P = .007, respectively), while PFJS increased from feedback to postfatigue feedback (P = .03). Knee range of motion increased from baseline to feedback (P = .043), then decreased from feedback to postfatigue feedback (P < .001). Visual feedback of GRF may reduce PFJS, but may not effectively transfer to a fatigued state.

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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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Dennis E. Anderson and Michael L. Madigan

Maintenance of healthy bone mineral density (BMD) is important for preventing fractures in older adults. Strains experienced by bone in vivo stimulate remodeling processes, which can increase or decrease BMD. However, there has been little study of age differences in bone strains. This study examined the relative contributions of age-related differences in femoral loading and BMD to age-related differences in femoral strains during walking using gait analysis, static optimization, and finite element modeling. Strains in older adult models were similar or larger than in young adult models. Reduced BMD increased strains in a fairly uniform manner, whereas older adult loading increased strains in early stance but decreased strains in late stance. Peak ground reaction forces, hip joint contact forces, and hip flexor forces were lower in older adults in late stance phase, and this helped older adults maintain strains similar to those of young adults despite lower BMD. Because walking likely represents a “baseline” level of stimulus for bone remodeling processes, increased strains during walking in older adults might indicate the extent of age-related impairment in bone remodeling processes. Such a measure might be clinically useful if it could be accurately determined with age-appropriate patient-specific loading, geometry, and BMD.

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

University, Stanford, CA) for the representative stride of each subject. Muscle forces were estimated in MATLAB (The Mathworks Inc, Natick, MA) using a static optimization algorithm. Medial and lateral axial tibiofemoral contact forces were computed using a frontal plane moment balance. 23 This model has

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Chen Deng, Jason C. Gillette and Timothy R. Derrick

implemented in MATLAB (MathWorks, Natick, MA) to estimate the dynamic muscle-tendon length and velocity adjusted maximal muscle forces, muscle moment arms and orientations for 44 lower limb muscles using the 3-dimensional segment angles obtained during the trials. Static optimization was used to select a set