Journal of biomechanics
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Journal of biomechanics · Dec 2020
Assessment of mechanical properties of articular cartilage with quantitative three-dimensional ultrashort echo time (UTE) cones magnetic resonance imaging.
Conventional magnetic resonance imaging (MRI) is not capable of detecting signal from the deep cartilage due to its short transverse relaxation time (T2). Moreover, several quantitative MRI techniques are significantly influenced by the magic angle effect. The combinations of ultrashort echo time (UTE) MRI with magnetization transfer (UTE-MT) and Adiabatic T1ρ (UTE-AdiabT1ρ) imaging allow magic angle-insensitive assessments of all regions of articular cartilage. ⋯ Correlations between other UTE MRI measurements (T2*, T1, and T2mm) and mechanical properties were non-significant. The 3D UTE-AdiabT1ρ and UTE-MT sequences were highlighted as promising surrogates for non-invasive assessment of cartilage mechanical properties. MMF from UTE-MT modeling showed the highest correlations with cartilage mechanics.
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Journal of biomechanics · Dec 2020
Spring-loaded inverted pendulum modeling improves neural network estimation of ground reaction forces.
Inertial-measurement-unit (IMU)-based wearable gait-monitoring systems provide kinematic information but kinetic information, such as ground reaction force (GRF) are often needed to assess gait symmetry and joint loading. Recent studies have reported methods for predicting GRFs from IMU measurement data by using artificial neural networks (ANNs). To obtain reliable predictions, the ANN requires a large number of measurement inputs at the cost of wearable convenience. ⋯ Leave-one-subject-out cross-validation was performed with normalized root mean square error and r as quantitative measures of prediction performance. The vertical and anteroposterior (AP) GRFs obtained using both methods agreed well with the experimental data, but Method 2 yielded improved predictions of AP GRF compared to Method 1 (p = 0.005). These results imply that knowledge of the dynamic characteristics of walking, combined with a neural network, could enhance the efficiency and accuracy of GRF prediction and help resolve the tradeoff between information richness and wearable convenience of wearable technologies.
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Journal of biomechanics · Dec 2020
Estimation of the ground reaction forces from a single video camera based on the spring-like center of mass dynamics of human walking.
In clinical studies, the ground reaction forces (GRFs) during walking have found being highly useful. Therefore, the force sensing shoes with small sensors and estimation methods based on kinematics from motion capture systems or inertial measurement units were proposed. Recent studies demonstrated methods of extracting GRFs from whole-body joint kinematics, which requires a significant computational load. ⋯ The GRF prediction errors were approximately 9-11%, with the best matching trials found to be at a self-selected gait speed. The prediction of anterior-posterior GRF components showed a more consistent match than the vertical GRF. The results demonstrated the possibility of marker-less kinetics prediction from video images incorporating the mechanical characteristics of the CoM.