Prediction Of Kinematics Research Articles

A comparative study of wave kinematics and inline forces on vertical cylinders under Draupner-type freak waves

This study focuses on predictions of wave kinematics and inline forces acting on vertical cylinders concerning a series of Draupner-type freak waves. Multiple hydrodynamic models were introduced by using different stretching models for wave kinematics and different inertia coefficients of the Morison formula, and their applicability was assessed by comparisons with experimental measurements. It demonstrates that the modified Wheeler stretching method (Molin, 2002) could give satisfactory predictions of particle velocities and inline forces of Draupner-type freak waves. The drag force term is not negligible for slender structure, and frequency-independent inertia coefficients are preferred in Morison predictions. The high-frequency force components of small quantities are found to be responsible for the discrepancies between predictions and measurements.

Sensitivity of Model-Based Predictions of Post-TKA Kinematic Behavior to Residual Errors in Ultrasound-Based Knee Collateral Ligament Strain Assessment.

Ultrasound-based ligament strain estimation shows promise in non-invasively assessing knee joint collateral ligament behavior and improving ligament balancing procedures. However, the impact of ultrasound-based strain estimation residual errors on in-silico arthroplasty predictions remains unexplored. We investigated the sensitivity of post-arthroplasty kinematic predictions to ultrasound-based strain estimation errors compared to clinical inaccuracies in implant positioning.Two cadaveric legs were submitted to active squatting, and specimen-specific rigid computer models were formulated. Mechanical properties of the ligament model were optimized to reproduce experimentally obtained tibiofemoral kinematics and loads with minimal error. Resulting remaining errors were comparable to the current state-of-the-art. Ultrasound-derived strain residual errors were then introduced by perturbing lateral collateral ligament (LCL) and medial collateral ligament (MCL) stiffness. Afterwards, the implant position was perturbed to match with the current clinical inaccuracies reported in the literature. Finally, the impact on simulated post-arthroplasty tibiofemoral kinematics was compared for both perturbation scenarios. Ultrasound-based errors minimally affected kinematic outcomes (mean differences < 0.73° in rotations, 0.1 mm in translations). Greatest differences occurred in external tibial rotations (-0.61° to 0.73° for MCL, -0.28° to 0.27° for LCL). Comparatively, changes in implant position had larger effects, with mean differences up to 1.95° in external tibial rotation and 0.7 mm in mediolateral translation. In conclusion, our study demonstrated that the ultrasound-based assessment of collateral ligament strains has the potential to enhance current computer-based pre-operative knee arthroplasty planning.

Prediction of Gait Kinematics and Kinetics: A Systematic Review of EMG and EEG Signal Use and Their Contribution to Prediction Accuracy.

Human-machine interfaces hold promise in enhancing rehabilitation by predicting and responding to subjects' movement intent. In gait rehabilitation, neural network architectures utilize lower-limb muscle and brain activity to predict continuous kinematics and kinetics during stepping and walking. This systematic review, spanning five databases, assessed 16 papers meeting inclusion criteria. Studies predicted lower-limb kinematics and kinetics using electroencephalograms (EEGs), electromyograms (EMGs), or a combination with kinematic data and anthropological parameters. Long short-term memory (LSTM) and convolutional neural network (CNN) tools demonstrated highest accuracies. EEG focused on joint angles, while EMG predicted moments and torque joints. Useful EEG electrode locations included C3, C4, Cz, P3, F4, and F8. Vastus Lateralis, Rectus Femoris, and Gastrocnemius were the most commonly accessed muscles for kinematic and kinetic prediction using EMGs. No studies combining EEGs and EMGs to predict lower-limb kinematics and kinetics during stepping or walking were found, suggesting a potential avenue for future development in this technology.

Decoding movement kinematics from EEG using an interpretable convolutional neural network

Continuous decoding of hand kinematics has been recently explored for the intuitive control of electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs). Deep neural networks (DNNs) are emerging as powerful decoders, for their ability to automatically learn features from lightly pre-processed signals. However, DNNs for kinematics decoding lack in the interpretability of the learned features and are only used to realize within-subject decoders without testing other training approaches potentially beneficial for reducing calibration time, such as transfer learning. Here, we aim to overcome these limitations by using an interpretable convolutional neural network (ICNN) to decode 2-D hand kinematics (position and velocity) from EEG in a pursuit tracking task performed by 13 participants. The ICNN is trained using both within-subject and cross-subject strategies, and also testing the feasibility of transferring the knowledge learned on other subjects on a new one. Moreover, the network eases the interpretation of learned spectral and spatial EEG features. Our ICNN outperformed most of the other state-of-the-art decoders, showing the best trade-off between performance, size, and training time. Furthermore, transfer learning improved kinematics prediction in the low data regime. The network attributed the highest relevance for decoding to the delta-band across all subjects, and to higher frequencies (alpha, beta, low-gamma) for a cluster of them; contralateral central and parieto-occipital sites were the most relevant, reflecting the involvement of sensorimotor, visual and visuo-motor processing. The approach improved the quality of kinematics prediction from the EEG, at the same time allowing interpretation of the most relevant spectral and spatial features.

Tuning in to a hip-hop beat: Pursuit eye movements reveal processing of biological motion

Smooth pursuit eye movements are mainly driven by motion signals to achieve their goal of reducing retinal motion blur. However, they can also show anticipation of predictable movement patterns. Oculomotor predictions may rely on an internal model of the target kinematics. Most investigations on the nature of those predictions have concentrated on simple stimuli, such as a decontextualized dot. However, biological motion is one of the most important visual stimuli in regulating human interaction and its perception involves integration of form and motion across time and space. Therefore, we asked whether there is a specific contribution of an internal model of biological motion in driving pursuit eye movements. Unlike previous contributions, we exploited the cyclical nature of walking to measure eye movement's ability to track the velocity oscillations of the hip of point-light walkers. We quantified the quality of tracking by cross-correlating pursuit and hip velocity oscillations. We found a robust correlation between signals, even along the horizontal dimension, where changes in velocity during the stepping cycle are very subtle. The inversion of the walker and the presentation of the hip-dot without context incurred the same additional phase lag along the horizontal dimension. These findings support the view that information beyond the hip-dot contributes to the prediction of hip kinematics that controls pursuit. We also found a smaller phase lag in inverted walkers for pursuit along the vertical dimension compared to upright walkers, indicating that inversion does not simply reduce prediction. We suggest that pursuit eye movements reflect the visual processing of biological motion and as such could provide an implicit measure of higher-level visual function.

Lower Extremity Validation of a Human Body Model for High Rate Axial Loading in the Underbody Blast Environment.

While the use of Human Body Models (HBMs) in the underbody blast (UBB) environment has increased and shown positive results, the potential of these models has not been fully explored. Obtaining accurate kinematic and kinetic response are necessary to better understand the injury mechanisms for military safety applications. The objective of this study was to validate the Global Human Body Models Consortium (GHBMC) M50 lower extremity using a combined objective rating scheme in vertical and horizontal high-rate axial loading. The model's lower extremity biomechanical response was compared to Post Mortem Human Subjects (PMHS) subjects for vertically and horizontally-applied high rate axial loading. Two distinct experimental setups were used for model validation, comprising a total of 33 distinct end points for validation. A combined Correlation and Analysis (CORA) score that incorporates CORA, time-to-peak (TTP) and peak magnitude of the experimental signals and ISO TS 18571 was used to evaluate the model response. For the horizontal impacts, the combined CORA scores were 0.80, 0.84, and 0.81 for compression, force, and strain respectively. For the vertical impacts combined CORA scores for the knee Z force, compression and heel Z displacement ranged from 0.70-0.81, 0.87-0.91, and 0.82-0.99 respectively. The GHBMC lower extremity model showed good agreement with PMHS experimental data in the horizontal and vertical loading environment in 33 unique tests. The accuracy is demonstrated by using the ISO TS 18571 standard and a combined CORA score that takes into consideration the peak and time to peak of the signal. The results of this study show that GHBMC v 6.0 HBM lower extremity can be used for kinetic and kinematic predictions in the UBB environment.

Molecular kinematic viscosity prediction of natural ester insulating oil based on sparse Machine learning models

The high viscosity property of natural ester insulating oils can be improved by means of molecular structure modification. However, it is difficult to find the optimal molecular structure that meets the conditions in the vast chemical space by relying on macroscopic experiments. Herein, the kinematic viscosity prediction models of triglyceride molecules based on sparse machine learning methods are proposed in this paper. Firstly, the molecular dynamics technique is used to simulate the kinematic viscosity (-20℃ to 40℃) of the main 20 triglyceride molecules in natural ester insulating oil, which provides extensive characterization data for subsequent model training. Secondly, the molecular descriptors are calculated for each triglyceride molecule based on density functional theory (DFT). Thirdly, four sparse feature selection methods (Boruta, RFECV, Pearson correlation coefficient, and mutual information) are used to identify the most relevant molecular descriptors for the kinematic viscosity of natural ester insulating oil and further analysis is performed. Finally, quantitative structure–property relationship (QSPR) models are constructed using automated machine learning methods to achieve efficient and accurate prediction of kinematic viscosity (average R2 reached 0.96). This study provides certain mechanistic explanations from the perspective of molecular descriptors and provides a predictive model basis for the viscosity modification of natural ester molecules, which serves as a theoretical guide to improve the kinematic viscosity of natural ester insulating oil.

Taking Locomotion Mode as Prior: One Algorithm-Enabled Gait Events and Kinematics Prediction on Various Terrains

Both the discrete motion states and continuous joint kinematics are essential for controlling assistive robots under changeable environmental conditions. However, few studies investigate both the discrete and continuous motion intents. This paper is the first work to propose an end-to-end motion intent decoding method that integrates recognition of discrete locomotion modes and prediction of continuous joint kinematics and gait events. First, we propose a data-driven approach to segment the transitional periods of the adjacent locomotion modes and determine their boundaries. Second, we build a CNN-LSTM network that utilizes locomotion mode classification as the prior of joint kinematics and gait events prediction and thus decodes discrete and continuous motion intents. Finally, we evaluate our method through extensive experiments and comparisons. The proposed method for locomotion mode recognition achieves an accuracy of 98.73% for steady-state and 97.53% for transitional periods. For knee angle prediction, the method achieves the ahead-of-time prediction with NRMSE lower than 8.41%, R-value higher than 0.93, and R-square higher than 0.88 for various prediction time. For gait event prediction, it predicts the future timing of events with an error of 8.46 ms for initial contact, 32.76 ms for heel off, and 8.91 ms for toe off. This study reveals the feasibility of predicting locomotion-based kinematics and gait events on various terrains. The rich motion intents studied in this work highlight the potential of a more detailed lower-limb rehabilitation system.

The impact of task context on predicting finger movements in a brain-machine interface.

A key factor in the clinical translation of brain-machine interfaces (BMIs) for restoring hand motor function will be their robustness to changes in a task. With functional electrical stimulation (FES) for example, the patient's own hand will be used to produce a wide range of forces in otherwise similar movements. To investigate the impact of task changes on BMI performance, we trained two rhesus macaques to control a virtual hand with their physical hand while we added springs to each finger group (index or middle-ring-small) or altered their wrist posture. Using simultaneously recorded intracortical neural activity, finger positions, and electromyography, we found that decoders trained in one context did not generalize well to other contexts, leading to significant increases in prediction error, especially for muscle activations. However, with respect to online BMI control of the virtual hand, changing either the decoder training task context or the hand's physical context during online control had little effect on online performance. We explain this dichotomy by showing that the structure of neural population activity remained similar in new contexts, which could allow for fast adjustment online. Additionally, we found that neural activity shifted trajectories proportional to the required muscle activation in new contexts. This shift in neural activity possibly explains biases to off-context kinematic predictions and suggests a feature that could help predict different magnitude muscle activations while producing similar kinematics.

Multibody Dynamics of Cross Groove Constant Velocity Ball Joints for High Performance Racing Applications

Accurate prediction of constant velocity joint transient contact loading and complex kinematics is necessary to improve joint design and prevent incurring failure from rolling contact fatigue. A detailed multibody dynamic model is presented for cross groove constant velocity joints used in high performance automotive racing applications, hitherto not reported in the open literature. The developed model includes detailed contact mechanics and friction models for all contacts made between the components. A novel semi-Hertzian contact model is adopted for the cage-race conjunction. Local contact kinematics is evaluated based on a methodology used in tribological analysis. The proposed model is validated against available computational models provided in literature for cross groove designs. Using the model, contact pressures occurring in the ball-race and ball-cage contacts are shown to reach levels as high as 4 and 5 GPa, respectively. The assumed value of friction coefficient is shown to have a strong influence on the predicted contact forces. Local contact motions are presented for the ball-race and ball-cage contacts, demonstrating complex motion which varies between pure rolling and pure sliding within a single cycle.

Concurrent and Continuous Prediction of Finger Kinetics and Kinematics via Motoneuron Activities.

Robust neural decoding of intended motor output is crucial to enable intuitive control of assistive devices, such as robotic hands, to perform daily tasks. Few existing neural decoders can predict kinetic and kinematic variables simultaneously. The current study developed a continuous neural decoding approach that can concurrently predict fingertip forces and joint angles of multiple fingers. We obtained motoneuron firing activities by decomposing high-density electromyogram (HD EMG) signals of the extrinsic finger muscles. The identified motoneurons were first grouped and then refined specific to each finger (index or middle) and task (finger force and dynamic movement) combination. The refined motoneuron groups (separate matrix) were then applied directly to new EMG data in real-time involving both finger force and dynamic movement tasks produced by both fingers. EMG-amplitude-based prediction was also performed as a comparison. We found that the newly developed decoding approach outperformed the EMG-amplitude method for both finger force and joint angle estimations with a lower prediction error (Force: 3.47±0.43 vs 6.64±0.69% MVC, Joint Angle: 5.40±0.50° vs 12.8±0.65°) and a higher correlation (Force: 0.75±0.02 vs 0.66±0.05, Joint Angle: 0.94±0.01 vs 0.5±0.05) between the estimated and recorded motor output. The performance was also consistent for both fingers. The developed neural decoding algorithm allowed us to accurately and concurrently predict finger forces and joint angles of multiple fingers in real-time. Our approach can enable intuitive interactions with assistive robotic hands, and allow the performance of dexterous hand skills involving both force control tasks and dynamic movement control tasks.

Analysis of the drag anchor behaviour at shallow depths

Understanding the effects of shallow behaviour is necessary for accurate kinematic predictions of drag anchors since original methods are mostly based only on deep failure mechanisms. Therefore, shallow flow channel-soil interactions characterized by varying tangential and normal bearing factors are used in plasticity analysis (PA) to capture the distinct shallow behaviour of the anchor. To provide a comparison, using the coupled Eulerian-Lagrangian (CEL) finite element method combined with the VUAMP subroutine to incorporate the mooring chain effect, the embedment of the drag anchor under the tension of the mooring chain is simulated. With a constant soil strength profile, the CEL results on the variation of drag force, rotation angle, and anchor trajectory agree well with calculations of PA that are mature for deeply embedded anchors. However, for shallow depths, similar results to the CEL can only be gained from PA considering shallow fluke-soil interactions. The effects of soil strength flow during installation, soil stability, soil sensitivity and fluke width are also investigated.

Searching for ejected supernova companions in the era of precise proper motion and radial velocity measurements

ABSTRACT The majority of massive stars are born in binaries, and most unbind upon the first supernova. With precise proper motion surveys such as Gaia, it is possible to trace back the motion of stars in the vicinity of young remnants to search for ejected companions. Establishing the fraction of remnants with an ejected companion, and the photometric and kinematic properties of these stars, offers unique insight into supernova progenitor systems. In this paper, we employ binary population synthesis to produce kinematic and photometric predictions for ejected secondary stars. We demonstrate that the unbound neutron star velocity distribution from supernovae in binaries closely traces the input kicks. Therefore, the observed distribution of neutron star velocities should be representative of their natal kicks. We evaluate the probability for any given filter, magnitude limit, minimum measurable proper motion (as a function of magnitude), temporal baseline, distance, and extinction that an unbound companion can be associated with a remnant. We compare our predictions with results from previous companion searches, and demonstrate that the current sample of stars ejected by the supernova of their companion can be increased by a factor of ∼5–10 with Gaia data release 3. Further progress in this area is achievable by leveraging the absolute astrometric precision of Gaia, and by obtaining multiple epochs of deep, high resolution near-infrared imaging with the Hubble Space Telescope, JWST and next-generation wide-field near-infrared observatories such as Euclid or the Nancy Grace Roman Space Telescope.

Bayesian Calibration of Computational Knee Models to Estimate Subject-Specific Ligament Properties, Tibiofemoral Kinematics, and Anterior Cruciate Ligament Force With Uncertainty Quantification.

High-grade knee laxity is associated with early anterior cruciate ligament (ACL) graft failure, poor function, and compromised clinical outcome. Yet, the specific ligaments and ligament properties driving knee laxity remain poorly understood. We described a Bayesian calibration methodology for predicting unknown ligament properties in a computational knee model. Then, we applied the method to estimate unknown ligament properties with uncertainty bounds using tibiofemoral kinematics and ACL force measurements from two cadaver knees that spanned a range of laxities; these knees were tested using a robotic manipulator. The unknown ligament properties were from the Bayesian set of plausible ligament properties, as specified by their posterior distribution. Finally, we developed a calibrated predictor of tibiofemoral kinematics and ACL force with their own uncertainty bounds. The calibrated predictor was developed by first collecting the posterior draws of the kinematics and ACL force that are induced by the posterior draws of the ligament properties and model parameters. Bayesian calibration identified unique ligament slack lengths for the two knee models and produced ACL force and kinematic predictions that were closer to the corresponding in vitro measurement than those from a standard optimization technique. This Bayesian framework quantifies uncertainty in both ligament properties and model outputs; an important step towards developing subject-specific computational models to improve treatment for ACL injury.

Dispersion of tracer particles by wave turbulence

We have investigated the dispersion of tracer particles by numerically simulating ensemble nonlinear gravity waves on the surface of an infinitely deep fluid. Using the concepts of passive Lagrangian markers and insights from weak wave turbulence theory, we validated fundamental predictions for particle kinematics associated with scaling laws and probability density functions. These results improve the understanding of wave-driven transport phenomena that depart far from equilibrium and the development of the high-fidelity models of transport processes in geophysical systems.

EMG-Based Estimation of Lower Limb Joint Angles and Moments Using Long Short-Term Memory Network.

One of the fundamental limitations in human biomechanics is that we cannot directly obtain joint moments during natural movements without affecting the motion. However, estimating these values is feasible with inverse dynamics computation by employing external force plates, which can cover only a small area of the plate. This work investigated the Long Short-Term Memory (LSTM) network for the kinetics and kinematics prediction of human lower limbs when performing different activities without using force plates after the learning. We measured surface electromyography (sEMG) signals from 14 lower extremities muscles to generate a 112-dimensional input vector from three sets of features: root mean square, mean absolute value, and sixth-order autoregressive model coefficient parameters for each muscle in the LSTM network. With the recorded experimental data from the motion capture system and the force plates, human motions were reconstructed in a biomechanical simulation created using OpenSim v4.1, from which the joint kinematics and kinetics from left and right knees and ankles were retrieved to serve as output for training the LSTM. The estimation results using the LSTM model deviated from labels with average R2 scores (knee angle: 97.25%, knee moment: 94.9%, ankle angle: 91.44%, and ankle moment: 85.44%). These results demonstrate the feasibility of the joint angle and moment estimation based solely on sEMG signals for multiple daily activities without requiring force plates and a motion capture system once the LSTM model is trained.

Analyzing the impact of activation functions on the performance of the data-driven gait model

Long-term prediction of joint kinematics is an important factor for the development of advanced technology in the area of the prosthetic leg, biped robot, automotive industry, and human-robot collaboration. It is well-defined that the long-term prediction of joint kinematic angle for an uneven surface is a challenging task. In previous work, authors have employed deep learning techniques for kinematic modelling using the collected walking dataset on an uneven surface with multiple speeds. However, it is a challenging task to find the appropriate activation function for deep learning techniques. Therefore, this research work focuses on the suitability of the activation function for a data-driven gait model for multiple slopes and inclines as ground conditions. Twenty-five activation function with their time complexity is extensively studied. In addition, the fusion of standard error from the subject mean trajectory with conventional loss function is also presented. This fusion helps the training algorithm to train the data-driven model to follow the low variance point in the gait cycle more precisely. The result shows that the Sigmoid-weighted Linear Units (SiLU) activation function-based gait model outperforms the others based on the maximum error summary statistic. It is also established that the SiLU function-based gait model trained by fused objective function significantly improved the long-term prediction for two cases: (a) step change slope and (b) continuously varying slope.

A Scoping Review with Bibliometric Analysis of Para-Rowing: State of the Art and Future Directions.

Para-rowing is a format of rowing practiced by people with different types of disabilities, thanks to adapted equipment set-ups and regulations. Para-rowing made its debut recently at the 2008 Paralympic Games. According to the mandate of the "International Paralympic Committee", para-rowers should be enabled to pursue sporting excellence. Therefore, rigorous research is needed in terms of well-designed, high-quality studies. To the best of our knowledge, there are no systematic appraisals of the body of scholarly evidence in the field of para-rowing. As such, a scoping review enhanced by bibliometric analyses was carried out to provide a comprehensive synthesis of knowledge related to para-rowing for the perusal of practitioners and athletes. By mining eighteen major databases, 17 studies were retained in the present review. The included studies were found to focus on a range of aspects involving health, the etiology of injuries (n = 5), psychological and physiological responses (n = 5), performance, biomechanical analysis (n = 4), and new analytical approaches for kinematic assessments and predictions of mechanical outputs in para-rowers (n = 3). The scholarly community on para-rowing consists of 78 researchers, 16 (20.51%) of whom are highly interconnected. The most prolific author was Smoljanović T., from Croatia, with three items/documents. In total, 93.6% of scholars have authored one single document. Topological features indicated a highly fragmented and dispersed, poorly connected community characterized by a high number of clusters and a low strength of connections. In terms of publication years, the first scholarly article dates back to 2008, with four articles (23.5%) published in the current year, showing an increasing interest in this para-sports discipline. Finally, gaps in current research on para-rowing were identified in terms of overlooked topics, including sports nutrition, doping, and psychological aspects in para-rowers other than those with visual impairment.

Phase‐field Fracture Based on Representative Crack Elements (RCE): Inelastic Materials, Friction, Finite Deformations, Multi‐physics

AbstractThe concept of representative crack elements has been successfully introduced to phase‐field fracture in previous publications, where anisotropic elasticity [1], visco‐elasticity [2], elasto‐plasticity and crack surface friction [3] are considered at small deformations. This framework allows to overcome unrealistic predictions for the crack kinematics, reported e.g. in Strobl and Seelig [4], and Steinke and Kaliske [5], known from phase‐field models with approximations for the crack closure behaviour based on volumetric‐deviatoric, spectral or similar splits of stress or strain tensors.In the current contribution to the method of phase‐field fracture, the framework for representative crack elements is shown for finite deformations and fully coupled thermo‐mechanics. The iterative solution scheme for the representative crack element and the variational homogenisation method are sketched. Applications to elasto‐plasticity, crack surface friction, finite deformations and thermo‐mechanics with heat radiation through the crack demonstrate the flexibility of the framework.

Propagating spatiotemporal activity patterns across macaque motor cortex carry kinematic information

Propagating spatiotemporal neural patterns are widely evident across sensory, motor, and association cortical areas. However, it remains unclear whether any characteristics of neural propagation carry information about specific behavioral details. Here, we provide the first evidence for a link between the direction of cortical propagation and specific behavioral features of an upcoming movement on a trial-by-trial basis. We recorded local field potentials (LFPs) from multielectrode arrays implanted in the primary motor cortex of two rhesus macaque monkeys while they performed a 2D reach task. Propagating patterns were extracted from the information-rich high-gamma band (200 to 400Hz) envelopes in the LFP amplitude. We found that the exact direction of propagating patterns varied systematically according to initial movement direction, enabling kinematic predictions. Furthermore, characteristics of these propagation patterns provided additional predictive capability beyond the LFP amplitude themselves, which suggests the value of including mesoscopic spatiotemporal characteristics in refining brain-machine interfaces.