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The meniscus plays a crucial role in the proper functioning of the knee joint, and when it becomes damaged, partial removal or replacement is necessary to restore proper function. Understanding the stress and deformation of the meniscus during various movements is essential for developing effective materials for meniscus repair. However, accurately estimating the contact mechanics of the knee joint can be challenging due to its complex shape and the dynamic changes it undergoes during movement. To address this issue, the open-source software SCONE can be used to establish a kinematics model that monitors the different states of the knee joint during human motion and obtains relevant gait kinematics data. To evaluate the stress and deformation of the meniscus during normal human movement, values of different states in the movement gait can be selected for finite element analysis (FEA) of the knee joint. This analysis enables researchers to assess changes in the meniscus. To evaluate meniscus damage, it is necessary to obtain changes in its mechanical behavior during abnormal movements. This information can serve as a reference for designing and optimizing the mechanical performance of materials used in meniscus repair and replacement. [ABSTRACT FROM AUTHOR]

Clustering algorithms such as k-means and agglomerative hierarchical clustering (HCA) may provide a unique opportunity to analyze time-series kinematic data. Here we present an approach for determining number of clusters and which clustering algorithm to use on time-series lumbar and pelvis kinematic data. Cluster evaluation measures such as silhouette coefficient, elbow method, Dunn Index, and gap statistic were used to evaluate the quality of decision making. The result show that multiple clustering evaluation methods should be used to determine the ideal number of clusters and algorithm suitable for clustering time-series data for each dataset being analyzed. [ABSTRACT FROM AUTHOR]

The objective of this study was to assess the biofidelity of the Global Human Body Models Consortium (GHBMC) 50th male (M50-O) v6.0 seated in an upright (25-degree recline) all-belts-to-seat (ABTS) in a 56 km/h rear-facing frontal impact. The experimental boundary conditions from the post-mortem human subjects (PMHS) tests were replicated in the computational finite element (FE) environment. The performance of the rigidized FE ABTS model obtained from the original equipment manufacturer was validated via simulations using a Hybrid III FE model and comparison with experiments. Biofidelity of the GHBMC M50-O was evaluated using the most updated NHTSA Biofidelity Ranking System (BRS) method, where a biofidelity score under 2 indicates that the GHBMC response varies from the mean PMHS response by less than two standard deviations, suggesting good biofidelity. The GHBMC M50-O received an occupant response score and a seat loading score of 1.71 and 1.44, respectively. Head (BRS = 0.93) and pelvis (BRS = 1.29) resultant accelerations, and T-spine (avg. BRS = 1.55) and pelvis (BRS = 1.66) y-angular velocities were similar to the PMHS. The T-spine resultant accelerations (avg. BRS = 1.93) and head (BRS = 2.82), T1 (BRS = 2.10) and pelvis (BRS = 2.10) Z-displacements were underestimated in the GHBMC. Peak chest deflection in the anterior-posterior deflection in the GHBMC matched with the PMHS mean, however, the relative upward motion of abdominal contents and subsequent chest expansion were not observed in the GHBMC. Updates to the GHBMC M50-O towards improved thorax kinematics and mobility of abdominal organs should be considered to replicate PMHS characteristics more closely. [ABSTRACT FROM AUTHOR]

The purpose of this study was to develop a machine learning model to reconstruct time series kinematic and kinetic profiles of the ankle and knee joint across six different tasks using an ankle-mounted IMU. Four male collegiate basketball players performed repeated tasks, including walking, jogging, running, sidestep cutting, max-height jumping, and stop-jumping, resulting in a total of 102 movements. Ankle and knee flexion-extension angles and moments were estimated using motion capture and inverse dynamics and considered 'actual data' for the purpose of model fitting. Synchronous acceleration and angular velocity data were collected from right ankle-mounted IMUs. A time-series feature extraction model was used to determine a set of features used as input to a random forest regression model to predict the ankle and knee kinematics and kinetics. Five-fold cross-validation was performed to verify the model accuracy, and statistical parametric mapping was used to determine the difference between the predicted and experimental time series. The random forest regression model predicted the time-series profiles of the ankle and knee flexion-extension angles and moments with high accuracy (Kinematics: R2 ranged from 0.782 to 0.962, RMSE ranged from 2.19° to 11.58°; Kinetics: R2 ranged from 0.711 to 0.966, RMSE ranged from 0.10 Nm/kg to 0.41 Nm/kg). There were differences between predicted and actual time series for the knee flexion-extension moment during stop-jumping and walking. An appropriately trained feature-based regression model can predict time series knee and ankle joint angles and moments across a wide range of tasks using a single ankle-mounted IMU. [ABSTRACT FROM AUTHOR]

AbstractThis study investigated whether modes of variance in trial-to-trial whole-body kinematic variability identified by principal component analysis (PCA) were consistent across data pre-processing conditions generated from a common dataset. Comparisons made included 1) when trajectory data was expressed in a global vs. local reference frame; 2) when the number of landmarks used to represent whole-body motion differed, and; 3) whether input trajectory data were normalized to participant stature. Varying data pre-processing conditions prior to PCA does not bias the total variance identified. However, it can influence how modes of variance are dispersed across PCs, which in turn, can influence interpretation. [ABSTRACT FROM AUTHOR]

Despite availability of in vivo knee loads and kinematics data, conventional load- and displacement-controlled configurations still can't accurately predict tibiofemoral loads from kinematics or vice versa. We propose a combined load- and displacement-control method for joint-level simulations of the knee to reliably reproduce in vivo contact mechanics. Prediction errors of the new approach were compared to those of conventional purely load- or displacement-controlled models using in vivo implant loads and kinematics for multiple subjects and activities (CAMS-Knee dataset). Our method reproduced both loads and kinematics more closely than conventional models and thus demonstrates clear advantages for investigating tibiofemoral contact or wear. [ABSTRACT FROM AUTHOR]

Kinematic reconstruction of lower-limb movements using electroencephalography (EEG) has been used in several rehabilitation systems. However, the nonlinear relationship between neural activity and limb movement may challenge decoders in real-time Brain-Computer Interface (BCI) applications. This paper proposes a nonlinear neural decoder using an Unscented Kalman Filter (UKF) to infer lower-limb kinematics from EEG signals during pedaling. The results demonstrated maximum decoding accuracy using slow cortical potentials in the delta band (0.1-4 Hz) of 0.33 for Pearson's r-value and 8 for the signal-to-noise ratio (SNR). This leaves an open door to the development of closed-loop EEG-based BCI systems for kinematic monitoring during pedaling rehabilitation tasks. [ABSTRACT FROM AUTHOR]

AbstractIndustrial back support exoskeletons are a promising solution to alleviate lumbar musculoskeletal strain. Due to the complexity of spinal loading, evaluation of EMG data alone has been considered insufficient to assess their support effects, and complementary kinematic and dynamic data are required. However, the acquisition of marker-based kinematics is challenging with exoskeletons, as anatomical reference points, particularly on the pelvis, are occluded by exoskeleton structures. The aim of this study was therefore to develop and validate a method to reliably reconstruct the occluded pelvic markers. The movement data of six subjects, for whom pelvic markers could be placed while wearing an exoskeleton, were used to test the reconstructions and compare them to anatomical landmarks during lifting, holding and walking. Two separate approaches were used for the reconstruction. One used a reference coordinate system based on only exoskeleton markers (EXO), as has been suggested in the literature, while our proposed method adds a technical marker in the lumbar region (LUMB) to compensate for any shifting between exoskeleton and pelvis. Reconstruction with EXO yielded on average an absolute linear deviation of 54 mm ± 16 mm (mean ± 1SD) compared to anatomical markers. The additional marker in LUMB reduced mean deviations to 14 mm ± 7 mm (mean ± 1SD). Both methods were compared to reference values from the literature for expected variances due to marker placement and soft tissue artifacts. For LUMB 99% of reconstructions were within the defined threshold of 24 mm ±9 mm while for EXO 91% were outside. [ABSTRACT FROM AUTHOR]

Calculated intersegmental moments are commonly used in analyzing throwing movements. The inverse dynamics (ID) results can vary due to the chosen set of body segment inertia parameters (BSIP). A multitude of methods to determine BSIP sets are available. The purpose of this study was to clarify the influence of different estimation methods on the BSIPs and the respective impact on the ID results in javelin throwing. Movement kinematics were recorded for ten male javelin throwers. Six different methods were used to estimate BSIP sets for the upper extremities of each thrower. Subsequently, ID results were obtained for each thrower and BSIP set. Results show variations between 8% and 120% between the BSIP sets, and maximum intersegmental moments varied between 6% and 21%, respectively. Joint-specific variations of intersegmental moments were observed as well as movement-specific variations within a joint related to the different BSIP sets. Furthermore, the influence of BSIP sets appears to be subject-specific as well, with observed variations between 9% and 18% – some athletes are better represented by the chosen methods than others. Hence, our study results suggest that the method to determine BSIP sets needs to be carefully chosen for calculating joint kinetics in throwing movements. [ABSTRACT FROM AUTHOR]