Musculoskeletal Simulation Research Articles

Biomechanical joint loading mechanism of Tai Chi gait in individuals with knee osteoarthritis: A pilot simulation study

ObjectivesTai Chi (TC) has shown beneficial effects on joint function in knee osteoarthritis (OA). Biomechanical mechanisms of knee joint contact load (JCL) and muscle activations during TC are less understood. The purpose of this biomechanical simulation study was to examine JCL of TC gait, the most common used TC from and its causal interactions with muscle activations in knee OA. DesignSix knee OA and five healthy participants were recruited. Their full body motion of TC gait was collected. The JCL and muscle forces were quantified using a musculoskeletal simulation approach based on collected kinematics and kinetics. The JCL and muscle activations were compared between knee OA and healthy control group. In addition, the muscle contributions to the JCL were determined and compared between the two groups. ResultsKnee OA subjects had lower peak anterior-posterior shear forces and higher lateral shear forces than healthy control subjects during TC gait. Knee OA subjects also showed higher activations of knee flexor muscles than control subjects. Both knee extensor and flexors of the knee OA group were contributing to JCL and in the control group mainly the knee extensors. ConclusionsOur simulation results showed the JCL, muscle forces profiles, and muscle contributions to the JCL during TC gait in knee OA. The findings of this study provided a direct scientific link between JCL and muscle forces during TC gait in knee OA. This would allow us to develop more effective TC interventions for knee OA in the future.

Muscle Force Patterns in Lower Extremity Muscles for Elite Discus Throwers, Javelin Throwers and Shot-Putters - A Case Study.

Optimal release variables, as well as the kinematics and kinetics of athletes, are crucial for the maximization of throwing distance in athletics. Mathematical models and simulations allow throwing techniques to be studied. However, muscle force patterns and the contribution of specific muscle groups in athletics throwing events are not well understood and require detailed research. In this study, important variables of the muscle force generated during the javelin, discus and shot put events were determined using OpenSim software. Musculoskeletal simulations were carried out based on kinematic and kinetic data collected using the Vicon system and Kistler plates with the help of nine top Polish athletes (three in each event). OpenSim software was used to calculate muscle forces and joint velocities. For each discipline, it was found that the main muscle groups involved in the throwing movement were better at distinguishing throwers than joint velocities. The contribution of right ankle plantar flexors at the beginning of the final acceleration phase as well as left hip extensors at the end of the final acceleration phase was given special attention. This work provides a better understanding of the techniques used in athletics throws. Musculoskeletal simulations of throwing styles might help coaches analyze the techniques of individual athletes, resulting in better adjustment of training programmes and injury prevention protocols.

Effects of running in minimal, maximal and traditional running shoes: a musculoskeletal simulation exploration using statistical parametric mapping and Bayesian analyses

The current study aimed to use a musculoskeletal simulation approach to examine running biomechanics in minimal, maximal and traditional running shoes using a concurrent SPM and Bayesian approach. Thirteen male participants ran over a force platform at 4.0 m/s in minimal maximal and traditional running shoes. Lower extremity joint loading and muscle forces were explored using a musculoskeletal simulation approach. Differences between conditions were examined using statistical parametric mapping (SPM) and Bayesian one-way repeated measures ANOVA. Bayesian analyses showed that traditional running shoes increased vastus intermedius (208.8BW·ms), vastus lateralis (320.2BW·ms) vastus medialis (188.7BW·ms), lateral tibiofemoral (495.9BW·ms) and patellofemoral joint stress (1683.4KPa/BW·s) integrals compared to minimal running shoes (185.0BW·ms, 281.9BW·ms, 167.2BW·ms, 456.5BW·ms & 1524.9KPa/BW·s). Furthermore, SPM showed that minimal footwear increased glutaeal, medial tibiofemoral and hip forces during the first 10% of the stance phase and Achilles tendon forces from 20 to 40% stance compared to traditional running shoes, whereas Bayesian analysis showed that minimal footwear increased loading rates (366.9BW/s) compared to maximal and traditional running shoes. (186.5BW/s) and traditional running shoes (161.5BW/s). Finally, SPM also showed that maximal footwear enhanced ankle eversion from 10 to 30% of stance compared to both minimal and traditional running shoes. This study therefore shows that minimal footwear may place runners at increased risk from impact related chronic injuries yet attenuate risk from patellofemoral and lateral tibiofemoral pathologies compared to traditional running shoes. In addition, owing to increases in ankle eversion, maximal running shoes may enhance risk to the aetiology of medial tibial stress syndrome compared to minimal and traditional running shoes.

Impact of scaling errors of the thigh and shank segments on musculoskeletal simulation results

Musculoskeletal simulations are widely used in the research community. The locations of surface markers are mostly used to scale a generic model to the participant's anthropometry. Marker-based scaling approaches include errors due to inaccuracies in marker placements. How do scaling errors of the thigh and shank segments influence simulation results? Motion capture data and magnetic resonance images from a child with cerebral palsy and a typically developing child were used to create a subject-specific reference model for each child. These reference models were modified to mimic scaling errors due to inaccurately placed lateral epicondyle markers, which are frequently used to scale the thigh and shank segments. The thigh length was altered in 1 % steps from the original length and the shank length was accordingly adjusted to keep the total leg length constant. Thirty additional models were created, which included models with an altered thigh length of ±15 %. Subsequently, musculoskeletal simulations with OpenSim were performed with all models. Joint kinematics, joint kinetics, muscle forces and joint contact forces (JCF) were compared between the reference and altered models. The investigated scaling error influenced joint kinematics and joint kinetics by up to 9.4° (hip flexion angle) and 0.15 Nm/kg (knee flexion moment), respectively. Maximum muscle and JCF differences of 46 % (medial gastrocnemius) and 72 % (hip JCF) bodyweight, respectively, were observed between the reference and altered models. Scaling errors mainly changed the magnitude but not the shape of most analyzed parameters. The influence of scaling errors on simulation results were similar in both participants. Scaling errors of the thigh segment influence simulation results at all joints due to the global optimization approach used in musculoskeletal simulations. Our findings can be used to estimate potential errors due to marker-based scaling approaches in previous and future studies.

Hand Motion Capture from a 3D Leap Motion Controller for a Musculoskeletal Dynamic Simulation.

The AnyBody Modeling System™ (AMS) is a musculoskeletal software simulation solution using inverse dynamics analysis. It enables the determination of muscle and joint forces for a given bodily motion. The recording of the individual movement and the transfer into the AMS is a complex and protracted process. Researches indicated that the contactless, visual Leap Motion Controller (LMC) provides clinically meaningful motion data for hand tracking. Therefore, the aim of this study was to integrate the LMC hand motion data into the AMS in order to improve the process of recording a hand movement. A Python-based interface between the LMC and the AMS, termed ROSE Motion, was developed. This solution records and saves the data of the movement as Biovision Hierarchy (BVH) data and AnyScript vector files that are imported into the AMS simulation. Setting simulation parameters, initiating the calculation automatically, and fetching results is implemented by using the AnyPyTools library from AnyBody. The proposed tool offers a rapid and easy-to-use recording solution for elbow, hand, and finger movements. Features include animation, cutting/editing, exporting the motion, and remote controlling the AMS for the analysis and presentation of musculoskeletal simulation results. Comparing the motion tracking results with previous studies, covering problems when using the LMC limit the correctness of the motion data. However, fast experimental setup and intuitive and rapid motion data editing strengthen the use of marker less systems as the herein presented compared to marker based motion capturing.

Increased Q-factor increases medial compartment knee joint contact force during cycling

As Q-Factor (QF: inter-pedal distance) is increased, the internal knee abduction moment (KAbM) also increases, however it is unknown if this increased KAbM is associated with increased medial compartment knee joint contact force in cycling. In the absence of in vivo measurement, musculoskeletal modeling simulations may provide a viable option for estimating knee joint contact forces in cycling. The primary purpose of this study was to investigate the effect of increasing QF on knee joint total (TCF), and medial (MCF) compartment contact force during ergometer cycling. The secondary purpose was to evaluate whether KAbM and knee extension moment are accurate predictors of MCF in cycling. Musculoskeletal simulations were performed to estimate TCF and MCF for sixteen participants cycling at an original QF (150 mm), and wide QF (276 mm), at 80 W and 80 rotations per minute. Paired samples t-tests were used to detect differences between QF conditions. MCF increased significantly, however, TCF did not change at wide QF. Peak knee extensor muscle force did not change at wide QF. Peak knee flexor muscle force was significantly reduced with wide QF. Regression analyses showed KAbM and knee extension moments explained 87.4% of the variance in MCF when considered alongside QF. The increase of MCF may be attributed to increased frontal-plane pedal reaction force moment arm. Future research may seek to implement QF modulation as a part of rehabilitation or training procedures utilizing cycling in cases where medial compartment joint loading is of importance.

BOPS: a Matlab toolbox to batch musculoskeletal data processing for OpenSim

This paper presents Batch OpenSim Processing Scripts (BOPS), a Matlab toolbox for batch processing common OpenSim procedures: Inverse Kinematics, Inverse Dynamics, Muscle Analysis, Static Optimization, and Joint Reaction Analysis. BOPS is an easy-to-use and highly configurable tool that aims to reduce the time required to process large datasets, thus fostering the adoption of musculoskeletal modeling and simulations in daily practice. Its graphical user interface includes pre-defined setup files and has been designed to fulfill the needs of different research projects by simplifying the customization of the procedures, facilitating the analysis, and boosting research group collaborations. BOPS is released under Apache License 2.0, and its source code is freely available on SimTK and GitHub.

Musculoskeletal Simulation in terms of the mandibular movement

Musculoskeletal Simulation in terms of the mandibular movement

Task-dependent recruitment across ankle extensor muscles and between mechanical demands is driven by the metabolic cost of muscle contraction.

The nervous system is faced with numerous strategies for recruiting a large number of motor units within and among muscle synergists to produce and control body movement. This is challenging, considering multiple combinations of motor unit recruitment may result in the same movement. Yet vertebrates are capable of performing a wide range of movement tasks with different mechanical demands. In this study, we used an experimental human cycling paradigm and musculoskeletal simulations to test the theory that a strategy of prioritizing the minimization of the metabolic cost of muscle contraction, which improves mechanical efficiency, governs the recruitment of motor units within a muscle and the coordination among synergist muscles within the limb. Our results support our hypothesis, for which measured muscle activity and model-predicted muscle forces in soleus-the slower but stronger ankle plantarflexor-is favoured over the weaker but faster medial gastrocnemius (MG) to produce plantarflexor force to meet increased load demands. However, for faster-contracting speeds induced by faster-pedalling cadence, the faster MG is favoured. Similar recruitment patterns were observed for the slow and fast fibres within each muscle. By contrast, a commonly used modelling strategy that minimizes muscle excitations failed to predict force sharing and known physiological recruitment strategies, such as orderly motor unit recruitment. Our findings illustrate that this common strategy for recruiting motor units within muscles and coordination between muscles can explain the control of the plantarflexor muscles across a range of mechanical demands.

Deep Reinforcement Learning for Physics-Based Musculoskeletal Simulations of Healthy Subjects and Transfemoral Prostheses' Users During Normal Walking.

This paper proposes to use deep reinforcement learning for the simulation of physics-based musculoskeletal models of both healthy subjects and transfemoral prostheses' users during normal level-ground walking. The deep reinforcement learning algorithm is based on the proximal policy optimization approach in combination with imitation learning to guarantee a natural walking gait while reducing the computational time of the training. Firstly, the optimization algorithm is implemented for the OpenSim model of a healthy subject and validated with experimental data from a public data-set. Afterwards, the optimization algorithm is implemented for the OpenSim model of a generic transfemoral prosthesis' user, which has been obtained by reducing the number of muscles around the knee and ankle joints and, specifically, by keeping only the uniarticular ones. The model of the transfemoral prosthesis' user shows a stable gait, with a forward dynamic comparable to the healthy subject's, yet using higher muscles' forces. Even though the computed muscles' forces could not be directly used as control inputs for muscle-like linear actuators due to their pattern, this study paves the way for using deep reinforcement learning for the design of the control architecture of transfemoral prostheses.

Effects of spinal coupling and marker set on tracking of spine models during running

Despite the wide-spread use of musculoskeletal simulations and its use in estimating spinal loads, much is not known about how to best collect experimental data for modelling purposes. The primary purposes in this study were to determine the effects of tracking of running motion capture data to a model (1) with and without coupling of lumbar spine segments, and (2) with varying combinations of spinal markers. Running trials were collected from 7 participants, with each at three different speeds. The motion data was fit to the Full-Body Lumbar Spine Model (FBLS) with coupling of the lumbar spine enabled (CS) and disabled and therefore rigid (RS) in OpenSim through the Inverse Kinematics tool (IK). Different combinations of markers were chosen as tracking inputs for IK to represent experimental data collection with different marker sets. Root-mean-square (RMS) marker errors of all 13 markers along the spine for each gait cycle were calculated. The CS model resulted in 23.7% lower errors than the RS model (p < 0.001). The marker subset analysis showed that increasing the number of markers in the experimental data collection decreases the error, with the four marker tracking subsets with the highest number of markers tracked having the lowest errors. The location of the marker and timing in the gait cycle did not affect marker error. When spinal mechanics are of interest, the inclusion of a coupled lumbar spine in the model and a larger spinal marker set help better track experimental kinematics when fitting to a model.

Regression Equation between Required Force and Lumbar Load of Caregiver in Supporting Standing-up Motion via Computational Musculoskeletal Simulation

Caregivers experience low back pain because of patient handling such as supporting standing-up. The lumbar load of a caregiver depends on the required force for patient handling motions. If the relationship between the required force and the lumbar load is quantitatively clarified, it may be useful for preventing low back pain in caregivers. In this study, we investigated the quantitative relationships between the required force and lumbar loads such as vertebral stress and muscle activity in supporting standing-up by computational musculoskeletal simulation. First, a musculoskeletal model of a caregiver was prepared, and then the model performed simulated supporting standing-up motions. The vertical load used as the required force was placed on the upper limb of the model. The compressive/shear stress of the vertebral (L4–L5) and muscle activities of spinae erector muscle group were recorded as the lumbar load. The results showed that there are highly significant correlations between the required force (r &gt; 0.9, p &lt; 0.01). In addition, regression equations for predicting each lumbar load by the required force with highly determination coefficients (R2 &gt; 0.9) were obtained from these relationships. Furthermore, we found that when the required force was more than 120 N, the compression stresses of the vertebral exceeded injury threshold (3400 N) by the regression equation. These regression equations contribute to quantitatively consider lumbar loads of caregiver during patient handling based on injury thresholds and the required force.

OpenSim Moco: Musculoskeletal optimal control.

Musculoskeletal simulations are used in many different applications, ranging from the design of wearable robots that interact with humans to the analysis of patients with impaired movement. Here, we introduce OpenSim Moco, a software toolkit for optimizing the motion and control of musculoskeletal models built in the OpenSim modeling and simulation package. OpenSim Moco uses the direct collocation method, which is often faster and can handle more diverse problems than other methods for musculoskeletal simulation. Moco frees researchers from implementing direct collocation themselves—which typically requires extensive technical expertise—and allows them to focus on their scientific questions. The software can handle a wide range of problems that interest biomechanists, including motion tracking, motion prediction, parameter optimization, model fitting, electromyography-driven simulation, and device design. Moco is the first musculoskeletal direct collocation tool to handle kinematic constraints, which enable modeling of kinematic loops (e.g., cycling models) and complex anatomy (e.g., patellar motion). To show the abilities of Moco, we first solved for muscle activity that produced an observed walking motion while minimizing squared muscle excitations and knee joint loading. Next, we predicted how muscle weakness may cause deviations from a normal walking motion. Lastly, we predicted a squat-to-stand motion and optimized the stiffness of an assistive device placed at the knee. We designed Moco to be easy to use, customizable, and extensible, thereby accelerating the use of simulations to understand the movement of humans and other animals.

Predictions of Anterior Cruciate Ligament Dynamics From Subject-Specific Musculoskeletal Models and Dynamic Biplane Radiography.

In vivo knee ligament forces are important to consider for informing rehabilitation or clinical interventions. However, they are difficult to directly measure during functional activities. Musculoskeletal models and simulations have become the primary methods by which to estimate in vivo ligament loading. Previous estimates of anterior cruciate ligament (ACL) forces range widely, suggesting that individualized anatomy may have an impact on these predictions. Using ten subject-specific (SS) lower limb musculoskeletal models, which include individualized musculoskeletal geometry, muscle architecture, and six degree-of-freedom knee joint kinematics from dynamic biplane radiography (DBR), this study provides SS estimates of ACL force (anteromedial-aACL; and posterolateral-pACL bundles) during the full gait cycle of treadmill walking. These forces are compared to estimates from scaled-generic (SG) musculoskeletal models to assess the effect of musculoskeletal knee joint anatomy on predicted forces and the benefit of SS modeling in this context. On average, the SS models demonstrated a double force peak during stance (0.39-0.43 xBW per bundle), while only a single force peak during stance was observed in the SG aACL. No significant differences were observed between continuous SG and SS ACL forces; however, root mean-squared differences between SS and SG predictions ranged from 0.08 xBW to 0.27 xBW, suggesting SG models do not reliably reflect forces predicted by SS models. Force predictions were also found to be highly sensitive to ligament resting length, with ±10% variations resulting in force differences of up to 84%. Overall, this study demonstrates the sensitivity of ACL force predictions to SS anatomy, specifically musculoskeletal joint geometry and ligament resting lengths, as well as the feasibility for generating SS musculoskeletal models for a group of subjects to predict in vivo tissue loading during functional activities.

Muscle Eccentric Contractions Increase in Downhill and High-Grade Uphill Walking.

In Duchenne muscular dystrophy (DMD), one of the most severe and frequent genetic diseases in humans, dystrophic muscles are prone to damage caused by mechanical stresses during eccentric contractions. Eccentric contraction during walking on level ground likely contributes to the progression of degeneration in lower limb muscles. However, little is known about how the amount of muscle eccentric contractions is affected by uphill/downhill sloped walking, which is often encountered in patients’ daily lives and poses different biomechanical demands than level walking. By recreating the dynamic musculoskeletal simulations of downhill (−9°, −6°, and −3°), uphill (+3°, +6°, and +9°) and level walking (0°) from a published study of healthy participants, negative muscle mechanical work, as a measure of eccentric contraction, of 35 lower limb muscles was quantified and compared. Our results indicated that downhill walking overall induced more (32% at −9°, 19% at −6°, and 13% at −3°) eccentric contractions in lower limb muscles compared to level walking. In contrast, uphill walking led to eccentric contractions similar to level walking at low grades (+3° and +6°), but 17% more eccentric contraction at high grades (+9°). The changes of muscle eccentric contraction were largely predicted by the changes in both joint negative work and muscle coactivation in sloped walking. As muscle eccentric contractions play a critical role in the disease progression in DMD, this study provides an important baseline for future studies to safely improve rehabilitation strategies and exercise management for patients with DMD and other similar conditions.

Foot Rotation Gait Modifications Affect Hip and Ankle, But Not Knee, Stance Phase Joint Reaction Forces During Running.

Alterations of foot rotation angles have successfully reduced external knee adduction moments during walking and running. However, reductions in knee adduction moments may not result in reductions in knee joint reaction forces. The purpose of this study was to examine the effects of internal and external foot rotation on knee, hip, and ankle joint reaction forces during running. Motion capture and force data were recorded of 19 healthy adults running at 3.35 m/s during three conditions: (1) preferred (normal) and with (2) internal and (3) external foot rotation. Musculoskeletal simulations were performed using opensim and the Rajagopal 2015 model, modified to a two degree-of-freedom knee joint. Muscle excitations were derived using static optimization, including muscle physiology parameters. Joint reaction forces (i.e., the total force acting on the joints) were computed and compared between conditions using one-way analyses of variance (ANOVAs) via statistical parametric mapping (SPM). Internal foot rotation reduced resultant hip forces (from 18% to 23% stride), while external rotation reduced resultant ankle forces (peak force at 20% stride) during the stance phase. Three-dimensional and resultant knee joint reaction forces only differed at very early and very late stance phase. The results of this study indicate, similar to previous findings, that reductions in external knee adduction moments do not mirror reductions in knee joint reaction forces.

Imaging and Simulation of Inter-muscular Differences in Triceps Surae Contributions to Forward Propulsion During Walking.

Forward propulsion during the push-off phase of walking is largely governed at the ankle by differential neuromechanical contributions from the biarticular medial (MG) and lateral gastrocnemii (LG) and the uniarticular soleus (SOL). However, the relative contribution of these individual muscles to forward propulsion is equivocal, with important implications for the design and control of wearable assistive devices and for targeted therapeutics. The aim of this study was to evaluate the agreement between empirical and model-predicted triceps surae(i.e., MG, LG, and SOL) contributions to forward propulsion during walking using conditions that systematically manipulated both walking speed and the mechanical demand for forward propulsion at a fixed speed-through the use of aiding and impeding forces. Ten young adults (age: 24.1 ± 3.6 years, 6M/4F) participated. We found that muscle-specific responses derived from experimental measurements (i.e., activation and fascicle behavior) were consistent with those derived from musculoskeletal simulations (i.e., muscle force and positive mechanical work) within the same subjects. In vivo, compared to walking normally, only LG muscle activation was affected by both aiding and impeding forces. Similarly, increased propulsive demand elicited greater relative fascicle shortening in the MG but not the SOL. In silico, only MG and LG force and positive mechanical work increased significantly to meet the increased demands for forward propulsion. By combining electromyography, ultrasound imaging, and musculoskeletal modeling in the same subjects, our cumulative findings suggest that the biarticular gastrocnemius muscles play a more significant role than the uniarticular soleus in governing changes in forward propulsion during the mid to late stance phase of walking.

A multi-scale modelling framework combining musculoskeletal rigid-body simulations with adaptive finite element analyses, to evaluate the impact of femoral geometry on hip joint contact forces and femoral bone growth.

Multi-scale simulations, combining muscle and joint contact force (JCF) from musculoskeletal simulations with adaptive mechanobiological finite element analysis, allow to estimate musculoskeletal loading and predict femoral growth in children. Generic linearly scaled musculoskeletal models are commonly used. This approach, however, neglects subject- and age-specific musculoskeletal geometry, e.g. femoral neck-shaft angle (NSA) and anteversion angle (AVA). This study aimed to evaluate the impact of proximal femoral geometry, i.e. altered NSA and AVA, on hip JCF and femoral growth simulations. Musculoskeletal models with NSA ranging from 120° to 150° and AVA ranging from 20° to 50° were created and used to calculate muscle and hip JCF based on the gait analysis data of a typically developing child. A finite element model of a paediatric femur was created from magnetic resonance images. The finite element model was morphed to the geometries of the different musculoskeletal models and used for mechanobiological finite element analysis to predict femoral growth trends. Our findings showed that hip JCF increase with increasing NSA and AVA. Furthermore, the orientation of the hip JCF followed the orientation of the femoral neck axis. Consequently, the osteogenic index, which is a function of cartilage stresses and defines the growth rate, barely changed with altered NSA and AVA. Nevertheless, growth predictions were sensitive to the femoral geometry due to changes in the predicted growth directions. Altered NSA had a bigger impact on the growth results than altered AVA. Growth simulations based on mechanobiological principles were in agreement with reported changes in paediatric populations.

Knee Medial Compartment Joint Loads In Stationary Cycling With Increased Q-factor

As Q-Factor (QF: inter-pedal width) is increased, the knee abduction moment also increases. Though the knee abduction moment has been associated with knee medial compartment joint load, it is not a direct measure of joint contact forces. In the absence of in vivo measurement using an instrumented knee implant, musculoskeletal modeling simulations may provide a viable option for estimation of knee joint contact forces. PURPOSE: To estimate the total knee joint compressive force (TCF) and knee medial compartment joint compressive force (MCF) in stationary cycling with increasing QF using musculoskeletal simulation. METHODS: Five recreationally active males cycled on a stationary ergometer at a workrate of 80 Watts and a cadence of 80 rotations per minute at two QF: original QF (150mm), and wide QF (276mm). Wide QF was increased using pedal extenders. Three-dimensional kinematic data (240 Hz, Vicon) and pedal reaction forces using two custom instrumented bike pedals (1200 Hz, Kistler) were collected. A modified gait2392 model with a knee that includes hinge joints for the medial and lateral compartments was used to estimate muscle forces with static optimization and TCF and MCF with joint reaction analysis (3.3 OpenSim, SimTK, Stanford University). Paired samples t-test and Cohen’s d were used to detect differences between conditions. RESULTS: Peak TCF increased from original to wide QF (960.2 ± 258.2 N to 1117.3 ± 202.1 N ; p = 0.299; d = 0.54) and MCF increased from original to wide QF (792.2 ± 98.4 N to 1029.2 ± 315.5 N; p = 0.116; d = 0.89). CONCLUSION: Large standard deviations and small sample size may account for the lack of statistical significance, yet medium and large effect sizes may allude TCF and MCF increases with greater QF (Figure 1). It appears the majority of TCF is born by the medial compartment; TCF and MCF ranged from 1.46 and 1.14 BW for original and 1.75 to 1.42 BW for wide QF, respectively. These loads are much smaller than 2.0-2.5 BW found in walking and 4.0 BW in jogging.

Effects Of Foot Rotation On Knee Joint Reaction Forces During Running

PURPOSE: The purpose of this study was to examine the effects of internal and external foot rotation on knee joint reaction forces during running. METHODS: Motion capture and force data were recorded on nineteen healthy adult runners (22.3±4.0 years, 67.99±10.27 kg, and 1.77±0.10 m) running at 3.5 m/s with normal and maximal comfortable external (EXT) and internal (INT) foot rotation. Musculoskeletal simulations were performed using the Rajagopal 2015 model and OpenSim (SimTK). All data were low pass filtered at 10 Hz and normalized to a full stride (stance: 0-36% & swing: 37-100% stride). Models were scaled to each subject’s anthropometric parameters. Inverse dynamics were derived by combining inverse kinematics and force data. Muscle excitations were derived using Static Optimization, including muscle physiology parameters. Joint Reaction Forces were obtained by combining inverse kinematics, force data, and muscle excitations. Within-subjects ANOVAs via Statistical Parametric Mapping were used to determine differences in resultant knee joint reaction force waveforms. RESULTS: The EXT condition decreased early stance (0-2% stride) and late swing (92-100% stride) phase forces, but increased forces during early swing (44-46%) phase compared to normal. The INT condition reduced forces during very late swing phase (92-94% stride). The EXT reduced forces during early stance (0-1% stride) and late swing (97-100% stride) compared to INT. CONCLUSIONS: Despite the known success of altered foot rotation on reducing external knee abduction moments during walking and running, knee forces were only reduced during low-loading portions of running strides. Thus, it appears altered foot rotations does not improve knee loading during running.