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Journal articles on the topic 'Prediction Motion'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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1

Fan, Hehe, Linchao Zhu, and Yi Yang. "Cubic LSTMs for Video Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8263–70. http://dx.doi.org/10.1609/aaai.v33i01.33018263.

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Predicting future frames in videos has become a promising direction of research for both computer vision and robot learning communities. The core of this problem involves moving object capture and future motion prediction. While object capture specifies which objects are moving in videos, motion prediction describes their future dynamics. Motivated by this analysis, we propose a Cubic Long Short-Term Memory (CubicLSTM) unit for video prediction. CubicLSTM consists of three branches, i.e., a spatial branch for capturing moving objects, a temporal branch for processing motions, and an output branch for combining the first two branches to generate predicted frames. Stacking multiple CubicLSTM units along the spatial branch and output branch, and then evolving along the temporal branch can form a cubic recurrent neural network (CubicRNN). Experiment shows that CubicRNN produces more accurate video predictions than prior methods on both synthetic and real-world datasets.

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2

Rudenko, Andrey, Luigi Palmieri, Michael Herman, Kris M. Kitani, Dariu M. Gavrila, and Kai O. Arras. "Human motion trajectory prediction: a survey." International Journal of Robotics Research 39, no. 8 (June 7, 2020): 895–935. http://dx.doi.org/10.1177/0278364920917446.

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With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand, and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots, and advanced surveillance systems. This article provides a survey of human motion trajectory prediction. We review, analyze, and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.

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3

Winkelstein, Beth A., and Barry S. Myers. "Importance of Nonlinear and Multivariable Flexibility Coefficients in the Prediction of Human Cervical Spine Motion." Journal of Biomechanical Engineering 124, no. 5 (September 30, 2002): 504–11. http://dx.doi.org/10.1115/1.1504098.

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The flexibility matrix currently forms the basis for multibody dynamics models of cervical spine motion. While studies have aimed to determine cervical motion segment behavior, their accuracy and utility have been limited by both experimental and analytical assumptions. Flexibility terms have been primarily represented as constants despite the spine’s nonlinear stiffening response. Also, nondiagonal terms, describing coupled motions, of the matrices are often omitted. Currently, no study validates the flexibility approach for predicting vertebral motions; nor have the effects of matrix approximations and simplifications been quantified. Therefore, the purpose of this study is to quantify flexibility relationships for cervical motion segments, examine the importance of nonlinear components of the flexibility matrix, and determine the extent to which multivariable relationships may alter motion prediction. To that end, using unembalmed human cervical spine motion segments, a full battery of flexibility tests were performed for a neutral orientation and also following an axial pretorque. Primary and coupled matrix components were described using linear and piecewise nonlinear incremental constants. A third matrix approach utilized multivariable incremental relationships. Measured motions were predicted using structural flexibility methods and evaluated using RMS error between predicted and measured responses. A full set of flexibility relationships describe primary and coupled motions for C3-C4 and C5-C6. A flexibility matrix using piecewise incremental responses offers improved predictions over one using linear methods (p<0.01). However, no significant improvement is obtained using nonlinear terms represented by a multivariable functional approach (p<0.2). Based on these findings, it is suggested that a multivariable approach for flexibility is more demanding experimentally and analytically while not offering improved motion prediction.

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4

Fernandes, J. M., and C. E. de Souza. "Ship Motion Prediction." IFAC Proceedings Volumes 26, no. 2 (July 1993): 881–85. http://dx.doi.org/10.1016/s1474-6670(17)48598-3.

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5

Ernst, Floris, Alexander Schlaefer, and Achim Schweikard. "Predicting the outcome of respiratory motion prediction." Medical Physics 38, no. 10 (September 22, 2011): 5569–81. http://dx.doi.org/10.1118/1.3633907.

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6

Fridovich-Keil, David, Andrea Bajcsy, Jaime F. Fisac, Sylvia L. Herbert, Steven Wang, Anca D. Dragan, and Claire J. Tomlin. "Confidence-aware motion prediction for real-time collision avoidance1." International Journal of Robotics Research 39, no. 2-3 (June 24, 2019): 250–65. http://dx.doi.org/10.1177/0278364919859436.

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One of the most difficult challenges in robot motion planning is to account for the behavior of other moving agents, such as humans. Commonly, practitioners employ predictive models to reason about where other agents are going to move. Though there has been much recent work in building predictive models, no model is ever perfect: an agent can always move unexpectedly, in a way that is not predicted or not assigned sufficient probability. In such cases, the robot may plan trajectories that appear safe but, in fact, lead to collision. Rather than trust a model’s predictions blindly, we propose that the robot should use the model’s current predictive accuracy to inform the degree of confidence in its future predictions. This model confidence inference allows us to generate probabilistic motion predictions that exploit modeled structure when the structure successfully explains human motion, and degrade gracefully whenever the human moves unexpectedly. We accomplish this by maintaining a Bayesian belief over a single parameter that governs the variance of our human motion model. We couple this prediction algorithm with a recently proposed robust motion planner and controller to guide the construction of robot trajectories that are, to a good approximation, collision-free with a high, user-specified probability. We provide extensive analysis of the combined approach and its overall safety properties by establishing a connection to reachability analysis, and conclude with a hardware demonstration in which a small quadcopter operates safely in the same space as a human pedestrian.

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7

Gülerce, Zeynep, Bahadır Kargoığlu, and Norman A. Abrahamson. "Turkey-Adjusted NGA-W1 Horizontal Ground Motion Prediction Models." Earthquake Spectra 32, no. 1 (February 2016): 75–100. http://dx.doi.org/10.1193/022714eqs034m.

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The objective of this paper is to evaluate the differences between the Next Generation Attenuation: West-1 (NGA-W1) ground motion prediction models (GMPEs) and the Turkish strong ground motion data set and to modify the required pieces of the NGA-W1 models for applicability in Turkey. A comparison data set is compiled by including strong motions from earthquakes that occurred in Turkey and earthquake metadata of ground motions consistent with the NGA-W1 database. Random-effects regression is employed and plots of the residuals are used to evaluate the differences in magnitude, distance, and site amplification scaling. Incompatibilities between the NGA-W1 GMPEs and Turkish data set in small-to-moderate magnitude, large distance, and site effects scaling are encountered. The NGA-W1 GMPEs are modified for the misfit between the actual ground motions and the model predictions using adjustments functions. Turkey-adjusted NGA-W1 models are compatible with the regional strong ground motion characteristics and preserve the well-constrained features of the global models.

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8

Jin, Xin, Jia Guo, Zhong Li, and Ruihao Wang. "Motion Prediction of Human Wearing Powered Exoskeleton." Mathematical Problems in Engineering 2020 (December 21, 2020): 1–8. http://dx.doi.org/10.1155/2020/8899880.

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With the development of powered exoskeleton in recent years, one important limitation is the capability of collaborating with human. Human-machine interaction requires the exoskeleton to accurately predict the human motion of the upcoming movement. Many recent works implement neural network algorithms such as recurrent neural networks (RNN) in motion prediction. However, they are still insufficient in efficiency and accuracy. In this paper, a Gaussian process latent variable model (GPLVM) is employed to transform the high-dimensional data into low-dimensional data. Combining with the nonlinear autoregressive (NAR) neural network, the GPLVM-NAR method is proposed to predict human motions. Experiments with volunteers wearing powered exoskeleton performing different types of motion are conducted. Results validate that the proposed method can forecast the future human motion with relative error of 2%∼5% and average calculation time of 120 s∼155 s, depending on the type of different motions.

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9

Hadian Jazi, Marjan, Alireza Bab-Hadiashar, and Reza Hoseinnezhad. "Analytical Analysis of Motion Separability." Scientific World Journal 2013 (2013): 1–15. http://dx.doi.org/10.1155/2013/878417.

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Motion segmentation is an important task in computer vision and several practical approaches have already been developed. A common approach to motion segmentation is to use the optical flow and formulate the segmentation problem using a linear approximation of the brightness constancy constraints. Although there are numerous solutions to solve this problem and their accuracies and reliabilities have been studied, the exact definition of the segmentation problem, its theoretical feasibility and the conditions for successful motion segmentation are yet to be derived. This paper presents a simplified theoretical framework for the prediction of feasibility, of segmentation of a two-dimensional linear equation system. A statistical definition of a separable motion (structure) is presented and a relatively straightforward criterion for predicting the separability of two different motions in this framework is derived. The applicability of the proposed criterion for prediction of the existence of multiple motions in practice is examined using both synthetic and real image sequences. The prescribed separability criterion is useful in designing computer vision applications as it is solely based on the amount of relative motion and the scale of measurement noise.

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10

Dürichen, R., T. Wissel, F. Ernst, A. Schlaefer, and A. Schweikard. "Multivariate respiratory motion prediction." Physics in Medicine and Biology 59, no. 20 (September 25, 2014): 6043–60. http://dx.doi.org/10.1088/0031-9155/59/20/6043.

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11

HIDA, Tomoya, Tetsuya ASANO, Chiharu HIGASHINO, Masaaki KANAMARU, Jun'ichi KANEKO, and Yoshimi TAKEUCHI. "0101 Development of Cutting Force Prediction Method Considering Cutting Tool Motion Error : Prediction of Surge Cutting Force Using Motion Information from CNC Controller." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2015.8 (2015): _0101–1_—_0101–5_. http://dx.doi.org/10.1299/jsmelem.2015.8._0101-1_.

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12

Gripenberg, Gustaf, and Ilkka Norros. "On the prediction of fractional Brownian motion." Journal of Applied Probability 33, no. 2 (September 1996): 400–410. http://dx.doi.org/10.2307/3215063.

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Integration with respect to the fractional Brownian motion Z with Hurst parameter is discussed. The predictor is represented as an integral with respect to Z, solving a weakly singular integral equation for the prediction weight function.

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13

Kordasiewicz, R. C., M. D. Gallant, and S. Shirani. "Affine Motion Prediction Based on Translational Motion Vectors." IEEE Transactions on Circuits and Systems for Video Technology 17, no. 10 (October 2007): 1388–94. http://dx.doi.org/10.1109/tcsvt.2007.903777.

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14

Mao, Wei, Miaomiao Liu, Mathieu Salzmann, and Hongdong Li. "Multi-level Motion Attention for Human Motion Prediction." International Journal of Computer Vision 129, no. 9 (June 16, 2021): 2513–35. http://dx.doi.org/10.1007/s11263-021-01483-7.

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15

Andersson, Kenneth, Mats Andersson, Peter Johansson, Robert Forchheimer, and Hans Knutsson. "Motion compensation using backward prediction and prediction refinement." Signal Processing: Image Communication 18, no. 5 (May 2003): 381–400. http://dx.doi.org/10.1016/s0923-5965(03)00012-2.

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16

Wang, Yu Chao, Fan Ming Liu, and Hui Xuan Fu. "Ship Rolling Motion Prediction Based on Wavelet Neural Network." Applied Mechanics and Materials 190-191 (July 2012): 724–28. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.724.

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The traditional time series predictive models are not able to achieve a satisfying prediction effect in the problem of a non-linear system and nonstationary time series. To solve these problems, ship course time series prediction, which is based on back propagation wavelet neural network structure and algorithm, was proposed. It combined wavelet analysis and neural network characteristics, and employed the nonlinear Morlet wavelet radices as the activation function. This method was applied to ship rolling motion prediction, and simulation results showed the validity to improving the prediction accuracy.

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17

de'Sperati, Claudio, and Ian M. Thornton. "Motion prediction at low contrast." Vision Research 154 (January 2019): 85–96. http://dx.doi.org/10.1016/j.visres.2018.11.004.

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18

Kappagantula, S., and K. Rao. "Motion Compensated Interframe Image Prediction." IEEE Transactions on Communications 33, no. 9 (1985): 1011–15. http://dx.doi.org/10.1109/tcom.1985.1096415.

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19

Baltay, Annemarie S., and Gregory C. Beroza. "Ground-motion prediction from tremor." Geophysical Research Letters 40, no. 24 (December 26, 2013): 6340–45. http://dx.doi.org/10.1002/2013gl058506.

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20

Wiegand, T., E. Steinbach, and B. Girod. "Affine multipicture motion-compensated prediction." IEEE Transactions on Circuits and Systems for Video Technology 15, no. 2 (February 2005): 197–209. http://dx.doi.org/10.1109/tcsvt.2004.841690.

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21

Jamjoom, Abdulrzaq Naji. "Motion Prediction of Underwater Sensors." European Journal of Engineering Research and Science 5, no. 10 (October 20, 2020): 1249–52. http://dx.doi.org/10.24018/ejers.2020.5.10.2177.

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In this work, we will simulate the motion of a single underwater sensor knowing the current velocity to predict its location and velocity during certain time frame using a numerical approach of non-linear time-dependent partial differential equations and develop numerical computer programming code to solve the equations. The underwater sensor are used to collect data for many scientific and practical reasons all the sensor collected data without specifying the sensor location and time will be missing lowers valuable information and by simulating the sensor motion numerically will have many values and impact on the underwater sensor industries as this will lead to less power consumption sensors with smaller size and less network coverage required. This paper will study the kinetics of the underwater sensor which will resulted to a set of non-linear time-dependent partial differential equations that can be solved analytically and computer programming simulation is developed to solve the equations and predict the motion of underwater sensor. Different scenarios considered in the work such as simulating the result for different sensor’s density and the effect on its final position. Also, the result will include the sensor velocity simulation and comparison with the sea current velocity. This work is limited to the motion prediction of single underwater sensor and the result is only for mechanical aspect of the problem, the networks connectivity or coverage is out-of-scope.

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22

Grottoli, Marco, Diane Cleij, Paolo Pretto, Yves Lemmens, Riender Happee, and Heinrich H. Bülthoff. "Objective evaluation of prediction strategies for optimization-based motion cueing." SIMULATION 95, no. 8 (December 6, 2018): 707–24. http://dx.doi.org/10.1177/0037549718815972.

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Optimization-based motion cueing algorithms based on model predictive control have been recently implemented to reproduce the motion of a car within the limited workspace of a driving simulator. These algorithms require a reference of the future vehicle motion to compute a prediction of the system response. Assumptions regarding the future reference signals must be made in order to develop effective prediction strategies. However, it remains unclear how the prediction of future vehicle dynamics influences the quality of the motion cueing. In this study two prediction strategies are considered. Oracle: the ideal prediction strategy that knows exactly what the future reference is going to be. Constant: a prediction strategy that ignores every future change and keeps the current vehicle’s linear accelerations and angular velocities constant. The two prediction strategies are used to reproduce a sequence of maneuvers between 0 and 50 km/h. A comparative analysis is carried out to objectively evaluate the influence of the prediction strategies on motion cueing quality. Dedicated indicators of correlation, delay and absolute error are used to compare the effects of the adopted prediction on simulator motion. Also the motion cueing mechanisms adopted by the different conditions are analyzed, together with the usage of simulator workspace. While the constant strategy provided reasonable cueing quality, the results show that knowledge of the future vehicle trajectory reduces the delay and improves correlation with the reference trajectory, it allows the combined usage of different motion cueing mechanisms and increases the usage of workspace.

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23

Bushong, Wednesday, Burcu Urgen, Luke Miller, and Ayse Saygin. "Influence of Form and Motion on Biological Motion Prediction." Journal of Vision 15, no. 12 (September 1, 2015): 500. http://dx.doi.org/10.1167/15.12.500.

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24

Ren, Qing, Seiko Nishioka, Hiroki Shirato, and Ross I. Berbeco. "Adaptive prediction of respiratory motion for motion compensation radiotherapy." Physics in Medicine and Biology 52, no. 22 (October 26, 2007): 6651–61. http://dx.doi.org/10.1088/0031-9155/52/22/007.

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25

Tesfamicael, B., T. Lee, and C. Keppel. "Prediction of Lung Tumor Motion With Measured Breathing Motion." International Journal of Radiation Oncology*Biology*Physics 84, no. 3 (November 2012): S735. http://dx.doi.org/10.1016/j.ijrobp.2012.07.1967.

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26

Jefferys, E. R., and B. S. Samra. "Adaptive Prediction of the Motion of Marine Vehicles." Journal of Energy Resources Technology 107, no. 4 (December 1, 1985): 450–54. http://dx.doi.org/10.1115/1.3231217.

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A predictor of the future motion of a vessel subject to random wave and wind forces, would have a variety of applications in ocean engineering. Most previous work has assumed that the wave spectrum is known and that the vessel is modeled accurately; both factors affect the predictor performance strongly. In practice, the relevant data is difficult to measure on a manoeuvering vessel and can change significantly with operating conditions. Here were describe the application of an adaptive algorithm which predicts the future of a signal from its history. The predictor adapts to the signal and varies its parameters to optimise the prediction. Operating on a signal with a stationary spectrum, the predictor tends to a steady performance; if the spectrum changes, the predictor quickly adjusts to the new situation. We illustrate the performance of the system with examples.

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27

Kundu, Jogendra Nath, Maharshi Gor, and R. Venkatesh Babu. "BiHMP-GAN: Bidirectional 3D Human Motion Prediction GAN." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8553–60. http://dx.doi.org/10.1609/aaai.v33i01.33018553.

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Human motion prediction model has applications in various fields of computer vision. Without taking into account the inherent stochasticity in the prediction of future pose dynamics, such methods often converges to a deterministic undesired mean of multiple probable outcomes. Devoid of this, we propose a novel probabilistic generative approach called Bidirectional Human motion prediction GAN, or BiHMP-GAN. To be able to generate multiple probable human-pose sequences, conditioned on a given starting sequence, we introduce a random extrinsic factor r, drawn from a predefined prior distribution. Furthermore, to enforce a direct content loss on the predicted motion sequence and also to avoid mode-collapse, a novel bidirectional framework is incorporated by modifying the usual discriminator architecture. The discriminator is trained also to regress this extrinsic factor r, which is used alongside with the intrinsic factor (encoded starting pose sequence) to generate a particular pose sequence. To further regularize the training, we introduce a novel recursive prediction strategy. In spite of being in a probabilistic framework, the enhanced discriminator architecture allows predictions of an intermediate part of pose sequence to be used as a conditioning for prediction of the latter part of the sequence. The bidirectional setup also provides a new direction to evaluate the prediction quality against a given test sequence. For a fair assessment of BiHMP-GAN, we report performance of the generated motion sequence using (i) a critic model trained to discriminate between real and fake motion sequence, and (ii) an action classifier trained on real human motion dynamics. Outcomes of both qualitative and quantitative evaluations, on the probabilistic generations of the model, demonstrate the superiority of BiHMP-GAN over previously available methods.

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28

Guo, Xiao, and Jongmoo Choi. "Human Motion Prediction via Learning Local Structure Representations and Temporal Dependencies." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2580–87. http://dx.doi.org/10.1609/aaai.v33i01.33012580.

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Human motion prediction from motion capture data is a classical problem in the computer vision, and conventional methods take the holistic human body as input. These methods ignore the fact that, in various human activities, different body components (limbs and the torso) have distinctive characteristics in terms of the moving pattern. In this paper, we argue local representations on different body components should be learned separately and, based on such idea, propose a network, Skeleton Network (SkelNet), for long-term human motion prediction. Specifically, at each time-step, local structure representations of input (human body) are obtained via SkelNet’s branches of component-specific layers, then the shared layer uses local spatial representations to predict the future human pose. Our SkelNet is the first to use local structure representations for predicting the human motion. Then, for short-term human motion prediction, we propose the second network, named as Skeleton Temporal Network (Skel-TNet). Skel-TNet consists of three components: SkelNet and a Recurrent Neural Network, they have advantages in learning spatial and temporal dependencies for predicting human motion, respectively; a feed-forward network that outputs the final estimation. Our methods achieve promising results on the Human3.6M dataset and the CMU motion capture dataset, and the code is publicly available 1.

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29

McCarthy, Dennis D. "Predicting Earth orientation." Symposium - International Astronomical Union 128 (1988): 275–80. http://dx.doi.org/10.1017/s007418090011959x.

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Predictions of Earth orientation parameters are affected by the accuracy of the input data, the quality of the statistical models, and the delay between the last observed data and the date of the first prediction. The accuracy of the prediction of polar motion is adequate to meet most user needs, but the prediction of UT1-UTC is more difficult. Extended forecasts of polar motion and the rotational time can also be made with useful accuracies.

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Luo, Albert C. J., and Fei-Yue Wang. "Nonlinear Dynamics of a Micro-Electro-Mechanical System With Time-Varying Capacitors." Journal of Vibration and Acoustics 126, no. 1 (January 1, 2004): 77–83. http://dx.doi.org/10.1115/1.1597211.

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The natural frequency and responses of a micro-electro-mechanical system (MEMS) with time-varying capacitors are determined under an equivalent direct current (DC) voltage. Under alternating current (AC) voltages, the resonant condition and the corresponding resonant motion possessing a wide energy band for such a system are investigated because the motion with the wide energy band is very easily sensed. For a given voltage strength, the AC frequency band is obtained for chaotic resonant motions in the specific resonant layer. The numerical and analytical predictions of such a motion are in a acceptable agreement, and the dynamic model provides the range prediction of the alternating current and voltage on the capacitor agreeing with experimental measurements. The lower-order resonant motion has a higher energy than the higher-order resonant motions, which indicates that the lower-order resonant motion can be easily sensed. Although this model is developed from a specified MEMS, the analysis and results can be applied to other dynamic systems.

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31

Kanokoda, Takahiro, Yuki Kushitani, Moe Shimada, and Jun Ichi Shirakashi. "Motion Prediction with Artificial Neural Networks Using Wearable Strain Sensors Based on Flexible Thin Graphite Films." Key Engineering Materials 826 (October 2019): 111–16. http://dx.doi.org/10.4028/www.scientific.net/kem.826.111.

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A human motion prediction system can be used to estimate human gestures in advance to the actual action for reducing delays in interactive system. We have already reported a method of simple and easy fabrication of strain sensors and wearable devices using pyrolytic graphite sheets (PGSs). The wearable electronics could detect various types of human motion, with high durability and fast response. In this study, we have demonstrated hand motion prediction by neural networks (NNs) using hand motion data obtained from data gloves based on PGSs. In our experiments, we measured hand motions of subjects for learning. We created 4-layered NNs to predict human hand motion in real-time. As a result, the proposed system successfully predicted hand motion in real-time. Therefore, these results suggested that human motion prediction system using NNs is able to forecast various types of human behavior using human motion data obtained from wearable devices based on PGSs.

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Yang, Xin Dong, Zuo Chao Wang, Ai Guo Shi, Bo Liu, and Li Li. "Research on Ship Swaying Motion Prediction Based on Multi-Variable Chaotic Time Series Analysis." Advanced Materials Research 712-715 (June 2013): 1550–54. http://dx.doi.org/10.4028/www.scientific.net/amr.712-715.1550.

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Wind and waves have particularly significant influence upon exertion of naval vessels battle effectiveness. It is urgently necessary to improve the ability of the Navy to carry out combat service in severe sea state normally. This paper aims to obtain the accurate prediction of ship motions with second level predictable time in real waves. According to the characteristics of the ship motion, the research on extremely short-time prediction of ship motion has been carried out based on multi-variable chaotic time series analysis, and the effectiveness of the prediction of ship motion in real wave is highly improved.

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de Wit, Matthieu M., and Laurel J. Buxbaum. "Critical Motor Involvement in Prediction of Human and Non-biological Motion Trajectories." Journal of the International Neuropsychological Society 23, no. 2 (February 2017): 171–84. http://dx.doi.org/10.1017/s1355617716001144.

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AbstractObjectives: Adaptive interaction with the environment requires the ability to predict both human and non-biological motion trajectories. Prior accounts of the neurocognitive basis for prediction of these two motion classes may generally be divided into those that posit that non-biological motion trajectories are predicted using the same motor planning and/or simulation mechanisms used for human actions, and those that posit distinct mechanisms for each. Using brain lesion patients and healthy controls, this study examined critical neural substrates and behavioral correlates of human and non-biological motion prediction. Methods: Twenty-seven left hemisphere stroke patients and 13 neurologically intact controls performed a visual occlusion task requiring prediction of pantomimed tool use, real tool use, and non-biological motion videos. Patients were also assessed with measures of motor strength and speed, praxis, and action recognition. Results: Prediction impairment for both human and non-biological motion was associated with limb apraxia and, weakly, with the severity of motor production deficits, but not with action recognition ability. Furthermore, impairment for human and non-biological motion prediction was equivalently associated with lesions in the left inferior parietal cortex, left dorsal frontal cortex, and the left insula. Conclusions: These data suggest that motor planning mechanisms associated with specific loci in the sensorimotor network are critical for prediction of spatiotemporal trajectory information characteristic of both human and non-biological motions. (JINS, 2017, 23, 171–184)

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Yang, Young Jun, and Sun Hong Kwon. "Prediction for Irregular Ocean Wave and Floating Body Motion by Regularization: Part 2. Motion Prediction." Transactions of FAMENA 41, no. 1 (April 26, 2017): 37–53. http://dx.doi.org/10.21278/tof.41104.

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35

Kim, Byungmoon, and Jarek Rossignac. "Collision Prediction." Journal of Computing and Information Science in Engineering 3, no. 4 (December 1, 2003): 295–301. http://dx.doi.org/10.1115/1.1632526.

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The prediction of collisions amongst N rigid objects may be reduced to a series of computations of the time to first contact for all pairs of objects. Simple enclosing bounds and hierarchical partitions of the space-time domain are often used to avoid testing object-pairs that clearly will not collide. When the remaining pairs involve only polyhedra under straight-line translation, the exact computation of the collision time and of the contacts requires only solving for intersections between linear geometries. When a pair is subject to a more general relative motion, such a direct collision prediction calculation may be intractable. The popular brute force collision detection strategy of executing the motion for a series of small time steps and of checking for static interferences after each step is often computationally prohibitive. We propose instead a less expensive collision prediction strategy, where we approximate the relative motion between pairs of objects by a sequence of screw motion segments, each defined by the relative position and orientation of the two objects at the beginning and at the end of the segment. We reduce the computation of the exact collision time and of the corresponding face/vertex and edge/edge collision points to the numeric extraction of the roots of simple univariate analytic functions. Furthermore, we propose a series of simple rejection tests, which exploit the particularity of the screw motion to immediately decide that some objects do not collide or to speed-up the prediction of collisions by about 30%, avoiding on average 3/4 of the root-finding queries even when the object actually collide.

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Xu, Chaoshun, Masahiro Fujiwara, Yasutoshi Makino, and Hiroyuki Shinoda. "Investigation of Preliminary Motions from a Static State and Their Predictability." Journal of Robotics and Mechatronics 33, no. 3 (June 20, 2021): 537–46. http://dx.doi.org/10.20965/jrm.2021.p0537.

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Humans observe the actions of others and predict their movements slightly ahead of time in everyday life. Many studies have been conducted to automate such a prediction ability computationally using neural networks; however, they implicitly assumed that preliminary motions occurred before significant movements. In this study, we quantitatively investigate when and how long a preliminary motion appears in motions from static states and what kinds of motion can be predicted in principle. We consider this knowledge fundamental for movement prediction in interaction techniques. We examined preliminary motions of basic movements such as kicking and jumping, and confirmed the presence of preliminary motions by using them as inputs to a neural network. As a result, although we did not find preliminary motion for a hand-moving task, a left-right jumping task had the most preliminary motion, up to 0.4 s before the main movement.

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Grunewald, A., and M. J. M. Lankheet. "The Orthogonal Motion Aftereffect." Perception 25, no. 1_suppl (August 1996): 65. http://dx.doi.org/10.1068/v96l0805.

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A recent model of motion perception suggests that the motion aftereffect (MAE) is due to an interaction across all directions, rather than just opposite directions (Grunewald, 1995 Perception24 Supplement, 111). According to the model, the MAE is caused by the interaction of broadly tuned inhibition and narrowly tuned excitation, both in direction space. The model correctly suggests that, after adaptation to opposite directions of motion, no MAE results. Unlike other accounts of the MAE, this model predicts that, after adaptation to opposite but broadly defined directions of motion, a MAE orthogonal to the inducing motions is observed. We tested this counter-intuitive prediction by adapting subjects to two populations of dots, whose average motion vectors were opposite, but which contained motion vectors deviating slightly (up to 30°) from the average direction. During the subsequent test phase, randomly moving dots were displayed. Subjects were asked to indicate whether they perceived any global motion during this phase, and if so, they were asked to indicate the perceived motion axis by aligning a line. Subjects were tested on four pairs of directions: vertical, horizontal, and the two diagonals. In all four conditions subjects reported seeing an MAE, and the axis that they indicated was always orthogonal to the inducing motions (ANOVA: p<0.001, accounted for 95% of variance). This experiment confirms the predictions made by the model, thus further supporting the interaction across all directions of narrowly tuned excitation and broadly tuned inhibition.

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Graves, Robert W., Brad T. Aagaard, and Kenneth W. Hudnut. "The ShakeOut Earthquake Source and Ground Motion Simulations." Earthquake Spectra 27, no. 2 (May 2011): 273–91. http://dx.doi.org/10.1193/1.3570677.

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The ShakeOut Scenario is premised upon the detailed description of a hypothetical Mw 7.8 earthquake on the southern San Andreas Fault and the associated simulated ground motions. The main features of the scenario, such as its endpoints, magnitude, and gross slip distribution, were defined through expert opinion and incorporated information from many previous studies. Slip at smaller length scales, rupture speed, and rise time were constrained using empirical relationships and experience gained from previous strong-motion modeling. Using this rupture description and a 3-D model of the crust, broadband ground motions were computed over a large region of Southern California. The largest simulated peak ground acceleration (PGA) and peak ground velocity (PGV) generally range from 0.5 to 1.0 g and 100 to 250 cm/s, respectively, with the waveforms exhibiting strong directivity and basin effects. Use of a slip-predictable model results in a high static stress drop event and produces ground motions somewhat higher than median level predictions from NGA ground motion prediction equations (GMPEs).

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Shin, Hyun-Kyoung, Jae-Hwan Jung, and Ho-Young Lee. "Prediction of Ship Maneuverability by Circular Motion Test." Journal of the Society of Naval Architects of Korea 46, no. 3 (June 20, 2009): 259–67. http://dx.doi.org/10.3744/snak.2009.46.3.259.

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Liu, Sun Bo, Ping An Shi, and Lei Wu. "Short-Term Prediction of Ship Motion Based on EMD-SVM." Applied Mechanics and Materials 571-572 (June 2014): 252–57. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.252.

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Ship sailing at sea is affected by many factors, such as winds, waves and so on, which makes six degrees of freedom motions and thus influences the shipboard arms control, aircraft landing and other operations. In view of the non-linear and non-stationary features of ship motion in waves, a new method based on EMD (Empirical Model Decomposition) and SVM (Support Vector Machine) is presented to predict the ship motion. The EMD is used to decompose the ship motion time series data into several IMFs (intrinsic mode functions) and a residual trend term, which decreases the difficulty of prediction. As the IMF is relatively stationary, but also non-linear, these features are fit to be processed by using SVM. Then the decompositions are used as inputs into SVM to forecast ship motion. The simulation and comparison analysis show that the EMD-SVM prediction model can effectively forecast the ship motion in waves.

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41

DeLucia, Patricia R., and Gregory W. Liddell. "Cognitive motion extrapolation and cognitive clocking in prediction motion tasks." Journal of Experimental Psychology: Human Perception and Performance 24, no. 3 (1998): 901–14. http://dx.doi.org/10.1037/0096-1523.24.3.901.

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Haas, Olivier C. L., Daniel Paluszczyszyn, Mariusz Ruta, and Piotr Skworcow. "Motion prediction and control for patient motion compensation in radiotherapy." IFAC Proceedings Volumes 44, no. 1 (January 2011): 5985–90. http://dx.doi.org/10.3182/20110828-6-it-1002.03559.

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Goda, Katsuichiro, and Gail M. Atkinson. "Variation of Source-to-Site Distance for Megathrust Subduction Earthquakes: Effects on Ground Motion Prediction Equations." Earthquake Spectra 30, no. 2 (May 2014): 845–66. http://dx.doi.org/10.1193/080512eqs254m.

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This study investigates the effects of using different finite-fault source models in evaluating rupture distances for megathrust subduction earthquakes. The uncertainty of the calculated rupture distances affects interpretation of the recorded ground motions significantly. To demonstrate this from an empirical perspective, ground motion data and available finite-fault models for the 2011 M9.0 Tohoku, 2003 M8.3 Tokachi-oki, and 2005 M7.2 Miyagi-oki earthquakes are analyzed. The impact of different finite-fault models on the development of ground motion prediction equations for these large subduction events is significant. Importantly, the results suggest that comparison of observed ground motion data with existing ground motion prediction models is not straightforward; different conclusions may be reached regarding agreement/disagreement between empirical data and developed models, depending on the selected finite-fault model. These results are particularly relevant to the development of ground motion prediction equations for subduction regions.

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Takao, Motoharu, Hiroaki Miyajima, and Takanori Shinagawa. "Diurnal modulation of visual motion prediction." Chronobiology International 32, no. 7 (July 9, 2015): 1019–23. http://dx.doi.org/10.3109/07420528.2015.1053564.

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Chen, Zhuo, Lu Wang, and Nelson H. C. Yung. "Adaptive human motion analysis and prediction." Pattern Recognition 44, no. 12 (December 2011): 2902–14. http://dx.doi.org/10.1016/j.patcog.2011.04.022.

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Sottinen, Tommi, and Lauri Viitasaari. "Prediction law of fractional Brownian motion." Statistics & Probability Letters 129 (October 2017): 155–66. http://dx.doi.org/10.1016/j.spl.2017.05.006.

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Barata, Catarina, Jacinto C. Nascimento, João M. Lemos, and Jorge S. Marques. "Sparse motion fields for trajectory prediction." Pattern Recognition 110 (February 2021): 107631. http://dx.doi.org/10.1016/j.patcog.2020.107631.

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Cheong, Daniel, Jon-Kar Zubieta, and Jing Liu. "Neural Correlates of Visual Motion Prediction." PLoS ONE 7, no. 6 (June 29, 2012): e39854. http://dx.doi.org/10.1371/journal.pone.0039854.

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Vetter, Petra, Marie-Helene Grosbras, and Lars Muckli. "TMS Over V5 Disrupts Motion Prediction." Cerebral Cortex 25, no. 4 (October 23, 2013): 1052–59. http://dx.doi.org/10.1093/cercor/bht297.

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Dockstader, S. L., and N. S. Imennov. "Prediction for human motion tracking failures." IEEE Transactions on Image Processing 15, no. 2 (February 2006): 411–21. http://dx.doi.org/10.1109/tip.2005.860594.

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