Comparison of Stance Phase Knee Joint Angles and Moments Using Two Different Surface Marker Representations of the Proximal Shank in Walkers and Runners

Efforts to compare different surface marker configurations in 3-dimensional motion analysis are warranted as more complex and custom marker sets become more common. At the knee, different markers can been used to represent the proximal shank. Often, two anatomical markers are placed over the femoral condyles, with their midpoint defining both the distal thigh and proximal shank segment ends. However, two additional markers placed over the tibial plateaus have been used to define the proximal shank end. For this experiment, simultaneous data for both proximal shank configurations were independently collected at two separate laboratories by different investigators, with one lab capturing a walking population and the other a running population. Common discrete knee joint variables were then compared between marker sets in each population. Using the augmented marker set, peak knee flexion after weight acceptance was less (1.2-1.7°, p<0.02) and peak knee adduction was greater (0.7-1.4°, p<0.001) in both data sets. Similarly, the calculated peak knee flexion moment was less by 15-20% and internal rotation moment was greater by 11-18% (p<0.001). These results suggest that the calculation of knee joint mechanics are influenced by the proximal shank’s segment endpoint definition, independent of dynamic task, investigator, laboratory environment, and population in this study

Deep Markov Random Field for Image Modeling

Markov Random Fields (MRFs), a formulation widely used in generative image modeling, have long been plagued by the lack of expressive power. This issue is primarily due to the fact that conventional MRFs formulations tend to use simplistic factors to capture local patterns. In this paper, we move beyond such limitations, and propose a novel MRF model that uses fully-connected neurons to express the complex interactions among pixels. Through theoretical analysis, we reveal an inherent connection between this model and recurrent neural networks, and thereon derive an approximated feed-forward network that couples multiple RNNs along opposite directions. This formulation combines the expressive power of deep neural networks and the cyclic dependency structure of MRF in a unified model, bringing the modeling capability to a new level. The feed-forward approximation also allows it to be efficiently learned from data. Experimental results on a variety of low-level vision tasks show notable improvement over state-of-the-arts.Comment: Accepted at ECCV 201

The effect of limb preference on braking strategy and knee joint mechanics during pivoting in female soccer players

Purpose: The purpose of this study was to explore whether limb preference influences braking strategy and knee joint mechanics during a 180° pivot task in female soccer players. Methods: Three-dimensional motion analyses of pivoting on the preferred and non-preferred kicking limbs were performed using 10 Qualisys ‘Oqus 7ʹ infrared cameras (240 Hz). Ground reaction forces (GRF) were collected from two AMTI force platforms (1200 Hz) embedded into the running track to examine penultimate (PEN) and final (FC) contact. Results: Both preferred and non-preferred limbs involved greater hip (ES = 2.85–3.81) and knee joint flexion angles (ES = 5.74–5.78) and peak vertical GRFs (ES = 0.87–1.61), but lower average vertical (ES = 2.55–3.01) and horizontal GRFs (ES = 3.05–3.67) during the PEN compared to the FC. Knee abduction angles were very likely greater (ES = 0.61) when turning off the non-preferred limb compared to the preferred limb. Conclusion: These findings may help us question the role of limb preference during pivoting, yet knee abduction angles and moments should be monitored with caution in female soccer players. Thus, it remains inconclusive the role limb preference plays in change of direction biomechanics for performance and risk of injury.<br/

Adjustments with running speed reveal neuromuscular adaptations during landing associated with high mileage running training.

It remains to be determined whether running training influences the amplitude of lower limb muscle activations before and during the first half of stance and whether such changes are associated with joint stiffness regulation and usage of stored energy from tendons. Therefore, the aim of this study was to investigate neuromuscular and movement adaptations before and during landing in response to running training across a range of speeds. Two groups of high mileage (HM; >45 km/wk, n = 13) and low mileage (LM; <15 km/wk, n = 13) runners ran at four speeds (2.5-5.5 m/s) while lower limb mechanics and electromyography of the thigh muscles were collected. There were few differences in prelanding activation levels, but HM runners displayed lower activations of the rectus femoris, vastus medialis, and semitendinosus muscles postlanding, and these differences increased with running speed. HM runners also demonstrated higher initial knee stiffness during the impact phase compared with LM runners, which was associated with an earlier peak knee flexion velocity, and both were relatively unchanged by running speed. In contrast, LM runners had higher knee stiffness during the slightly later weight acceptance phase and the disparity was amplified with increases in speed. It was concluded that initial knee joint stiffness might predominantly be governed by tendon stiffness rather than muscular activations before landing. Estimated elastic work about the ankle was found to be higher in the HM runners, which might play a role in reducing weight acceptance phase muscle activation levels and improve muscle activation efficiency with running training.NEW & NOTEWORTHY Although neuromuscular factors play a key role during running, the influence of high mileage training on neuromuscular function has been poorly studied, especially in relation to running speed. This study is the first to demonstrate changes in neuromuscular conditioning with high mileage training, mainly characterized by lower thigh muscle activation after touch down, higher initial knee stiffness, and greater estimates of energy return, with adaptations being increasingly evident at faster running speeds

Control of interjoint coordination during the swing phase of normal gait at different speeds

BACKGROUND: It has been suggested that the control of unconstrained movements is simplified via the imposition of a kinetic constraint that produces dynamic torques at each moving joint such that they are a linear function of a single motor command. The linear relationship between dynamic torques at each joint has been demonstrated for multijoint upper limb movements. The purpose of the current study was to test the applicability of such a control scheme to the unconstrained portion of the gait cycle – the swing phase. METHODS: Twenty-eight neurologically normal individuals walked along a track at three different speeds. Angular displacements and dynamic torques produced at each of the three lower limb joints (hip, knee and ankle) were calculated from segmental position data recorded during each trial. We employed principal component (PC) analysis to determine (1) the similarity of kinematic and kinetic time series at the ankle, knee and hip during the swing phase of gait, and (2) the effect of walking speed on the range of joint displacement and torque. RESULTS: The angular displacements of the three joints were accounted for by two PCs during the swing phase (Variance accounted for – PC1: 75.1 ± 1.4%, PC2: 23.2 ± 1.3%), whereas the dynamic joint torques were described by a single PC (Variance accounted for – PC1: 93.8 ± 0.9%). Increases in walking speed were associated with increases in the range of motion and magnitude of torque at each joint although the ratio describing the relative magnitude of torque at each joint remained constant. CONCLUSION: Our results support the idea that the control of leg swing during gait is simplified in two ways: (1) the pattern of dynamic torque at each lower limb joint is produced by appropriately scaling a single motor command and (2) the magnitude of dynamic torque at all three joints can be specified with knowledge of the magnitude of torque at a single joint. Walking speed could therefore be altered by modifying a single value related to the magnitude of torque at one joint

Computationally-Optimized Bone Mechanical Modeling from High-Resolution Structural Images

Image-based mechanical modeling of the complex micro-structure of human bone has shown promise as a non-invasive method for characterizing bone strength and fracture risk in vivo. In particular, elastic moduli obtained from image-derived micro-finite element (μFE) simulations have been shown to correlate well with results obtained by mechanical testing of cadaveric bone. However, most existing large-scale finite-element simulation programs require significant computing resources, which hamper their use in common laboratory and clinical environments. In this work, we theoretically derive and computationally evaluate the resources needed to perform such simulations (in terms of computer memory and computation time), which are dependent on the number of finite elements in the image-derived bone model. A detailed description of our approach is provided, which is specifically optimized for μFE modeling of the complex three-dimensional architecture of trabecular bone. Our implementation includes domain decomposition for parallel computing, a novel stopping criterion, and a system for speeding up convergence by pre-iterating on coarser grids. The performance of the system is demonstrated on a dual quad-core Xeon 3.16 GHz CPUs equipped with 40 GB of RAM. Models of distal tibia derived from 3D in-vivo MR images in a patient comprising 200,000 elements required less than 30 seconds to converge (and 40 MB RAM). To illustrate the system's potential for large-scale μFE simulations, axial stiffness was estimated from high-resolution micro-CT images of a voxel array of 90 million elements comprising the human proximal femur in seven hours CPU time. In conclusion, the system described should enable image-based finite-element bone simulations in practical computation times on high-end desktop computers with applications to laboratory studies and clinical imaging

Measuring Multi-Joint Stiffness during Single Movements: Numerical Validation of a Novel Time-Frequency Approach

This study presents and validates a Time-Frequency technique for measuring 2-dimensional multijoint arm stiffness throughout a single planar movement as well as during static posture. It is proposed as an alternative to current regressive methods which require numerous repetitions to obtain average stiffness on a small segment of the hand trajectory. The method is based on the analysis of the reassigned spectrogram of the arm's response to impulsive perturbations and can estimate arm stiffness on a trial-by-trial basis. Analytic and empirical methods are first derived and tested through modal analysis on synthetic data. The technique's accuracy and robustness are assessed by modeling the estimation of stiffness time profiles changing at different rates and affected by different noise levels. Our method obtains results comparable with two well-known regressive techniques. We also test how the technique can identify the viscoelastic component of non-linear and higher than second order systems with a non-parametrical approach. The technique proposed here is very impervious to noise and can be used easily for both postural and movement tasks. Estimations of stiffness profiles are possible with only one perturbation, making our method a useful tool for estimating limb stiffness during motor learning and adaptation tasks, and for understanding the modulation of stiffness in individuals with neurodegenerative diseases