Relevant bibliographies by topics / Human Motion Prediction

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Academic literature on the topic 'Human Motion Prediction'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Human Motion Prediction"

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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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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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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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Liu, Xiaoli, and Jianqin Yin. "Multi-Head TrajectoryCNN: A New Multi-Task Framework for Action Prediction." Applied Sciences 12, no. 11 (May 26, 2022): 5381. http://dx.doi.org/10.3390/app12115381.

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Action prediction is an important task in human activity analysis, which has many practical applications, such as human–robot interactions and autonomous driving. Action prediction often comprises two subtasks: action semantic prediction and future human motion prediction. Most of the existing works treat these subtasks separately, ignoring the correlations, leading to unsatisfying performance. By contrast, we jointly model these tasks and improve human motion predictions utilizing their action semantics. In terms of methodology, we propose a novel multi-task framework (Multi-head TrajectoryCNN) to simultaneously predict the action semantics and human motion of future human movements. Specifically, we first extract a general spatiotemporal representation of partial observations via two regression blocks. Then, we propose a regression head and a classification head for predicting future human motion and action semantics of human motion, respectively. For the regression head, another two stacked regression blocks and two convolutional layers are applied to predict future poses from the general representation learning. For the classification head, we propose a classification block and stack two regression blocks to predict action semantics from the general representation. In this way, the regression and classification heads are incorporated into a unified framework. During the backward propagation of the network, the human motion prediction and the semantic prediction may be enhanced by each other. NTU RGB+D is a widely used large-scale dataset for action recognition, which was collected by 40 different subjects from three views. Based on the official protocols, we use the skeletal modality and process action sequences with fixed lengths for the evaluation of our action prediction task. Experiments on NTU RGB+D show our model’s state-of-the-art performance. Furthermore, the experimental results also show that semantic information is of great help in predicting future human motion.
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Liu, Hongyi, and Lihui Wang. "Human motion prediction for human-robot collaboration." Journal of Manufacturing Systems 44 (July 2017): 287–94. http://dx.doi.org/10.1016/j.jmsy.2017.04.009.

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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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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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Liu, Zhenguang, Kedi Lyu, Shuang Wu, Haipeng Chen, Yanbin Hao, and Shouling Ji. "Aggregated Multi-GANs for Controlled 3D Human Motion Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 3 (May 18, 2021): 2225–32. http://dx.doi.org/10.1609/aaai.v35i3.16321.

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Human motion prediction from historical pose sequence is at the core of many applications in machine intelligence. However, in current state-of-the-art methods, the predicted future motion is confined within the same activity. One can neither generate predictions that differ from the current activity, nor manipulate the body parts to explore various future possibilities. Undoubtedly, this greatly limits the usefulness and applicability of motion prediction. In this paper, we propose a generalization of the human motion prediction task in which control parameters can be readily incorporated to adjust the forecasted motion. Our method is compelling in that it enables manipulable motion prediction across activity types and allows customization of the human movement in a variety of fine-grained ways. To this aim, a simple yet effective composite GAN structure, consisting of local GANs for different body parts and aggregated via a global GAN is presented. The local GANs game in lower dimensions, while the global GAN adjusts in high dimensional space to avoid mode collapse. Extensive experiments show that our method outperforms state-of-the-art. The codes are available at https://github.com/herolvkd/AM-GAN.
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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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Lyu, Kedi, Haipeng Chen, Zhenguang Liu, Beiqi Zhang, and Ruili Wang. "3D human motion prediction: A survey." Neurocomputing 489 (June 2022): 345–65. http://dx.doi.org/10.1016/j.neucom.2022.02.045.

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Dissertations / Theses on the topic "Human Motion Prediction"

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Lasota, Przemyslaw A. (Przemyslaw Andrzej). "Robust human motion prediction for safe and efficient human-robot interaction." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122497.

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Thesis: Ph. D. in Autonomous Systems, Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 175-188).
From robotic co-workers in factories to assistive robots in homes, human-robot interaction (HRI) has the potential to revolutionize a large array of domains by enabling robotic assistance where it was previously not possible. Introducing robots into human-occupied domains, however, requires strong consideration for the safety and efficiency of the interaction. One particularly effective method of supporting safe an efficient human-robot interaction is through the use of human motion prediction. By predicting where a person might reach or walk toward in the upcoming moments, a robot can adjust its motions to proactively resolve motion conflicts and avoid impeding the person's movements. Current approaches to human motion prediction, however, often lack the robustness required for real-world deployment. Many methods are designed for predicting specific types of tasks and motions, and do not necessarily generalize well to other domains.
It is also possible that no single predictor is suitable for predicting motion in a given scenario, and that multiple predictors are needed. Due to these drawbacks, without expert knowledge in the field of human motion prediction, it is difficult to deploy prediction on real robotic systems. Another key limitation of current human motion prediction approaches lies in deficiencies in partial trajectory alignment. Alignment of partially executed motions to a representative trajectory for a motion is a key enabling technology for many goal-based prediction methods. Current approaches of partial trajectory alignment, however, do not provide satisfactory alignments for many real-world trajectories. Specifically, due to reliance on Euclidean distance metrics, overlapping trajectory regions and temporary stops lead to large alignment errors.
In this thesis, I introduce two frameworks designed to improve the robustness of human motion prediction in order to facilitate its use for safe and efficient human-robot interaction. First, I introduce the Multiple-Predictor System (MPS), a datadriven approach that uses given task and motion data in order to synthesize a high performing predictor by automatically identifying informative prediction features and combining the strengths of complementary prediction methods. With the use of three distinct human motion datasets, I show that using the MPS leads to lower prediction error in a variety of HRI scenarios, and allows for accurate prediction for a range of time horizons. Second, in order to address the drawbacks of prior alignment techniques, I introduce the Bayesian ESTimator for Partial Trajectory Alignment (BEST-PTA).
This Bayesian estimation framework uses a combination of optimization, supervised learning, and unsupervised learning components that are trained and synthesized based on a given set of example trajectories. Through an evaluation on three human motion datasets, I show that BEST-PTA reduces alignment error when compared to state-of-the-art baselines. Furthermore, I demonstrate that this improved alignment reduces human motion prediction error. Lastly, in order to assess the utility of the developed methods for improving safety and efficiency in HRI, I introduce an integrated framework combining prediction with robot planning in time. I describe an implementation and evaluation of this framework on a real physical system. Through this demonstration, I show that the developed approach leads to automatically derived adaptive robot behavior. I show that the developed framework leads to improvements in quantitative metrics of safety and efficiency with the use of a simulated evaluation.
"Funded by the NASA Space Technology Research Fellowship Program and the National Science Foundation"--Page 6
by Przemyslaw A. Lasota.
Ph. D. in Autonomous Systems
Ph.D.inAutonomousSystems Massachusetts Institute of Technology, Department of Aeronautics and Astronautics
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Conte, Dean Edward. "Autonomous Robotic Escort Incorporating Motion Prediction with Human Intention." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/102581.

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This thesis presents a framework for a mobile robot to escort a human to their destination successfully and efficiently. The proposed technique uses accurate path prediction incorporating human intention to locate the robot in front of the human while walking. Human intention is inferred by the head pose, an effective past-proven implicit indicator of intention, and fused with conventional physics-based motion prediction. The human trajectory is estimated and predicted using a particle filter because of the human's nonlinear and non-Gaussian behavior, and the robot control action is determined from the predicted human pose allowing for anticipative autonomous escorting. Experimental analysis shows that the incorporation of the proposed human intention model reduces human position prediction error by approximately 35% when turning. Furthermore, experimental validation with an omnidirectional mobile robotic platform shows escorting up to 50% more accurate compared to the conventional techniques, while achieving 97% success rate.
Master of Science
This thesis presents a method for a mobile robot to escort a human to their destination successfully and efficiently. The proposed technique uses human intention to predict the walk path allowing the robot to be in front of the human while walking. Human intention is inferred by the head direction, an effective past-proven indicator of intention, and is combined with conventional motion prediction. The robot motion is then determined from the predicted human position allowing for anticipative autonomous escorting. Experimental analysis shows that the incorporation of the proposed human intention reduces human position prediction error by approximately 35% when turning. Furthermore, experimental validation with an mobile robotic platform shows escorting up to 50% more accurate compared to the conventional techniques, while achieving 97% success rate. The unique escorting interaction method proposed has applications such as touch-less shopping cart robots, exercise companions, collaborative rescue robots, and sanitary transportation for hospitals.
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Bataineh, Mohammad Hindi. "New neural network for real-time human dynamic motion prediction." Thesis, The University of Iowa, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3711174.

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Artificial neural networks (ANNs) have been used successfully in various practical problems. Though extensive improvements on different types of ANNs have been made to improve their performance, each ANN design still experiences its own limitations. The existing digital human models are mature enough to provide accurate and useful results for different tasks and scenarios under various conditions. There is, however, a critical need for these models to run in real time, especially those with large-scale problems like motion prediction which can be computationally demanding. For even small changes to the task conditions, the motion simulation needs to run for a relatively long time (minutes to tens of minutes). Thus, there can be a limited number of training cases due to the computational time and cost associated with collecting training data. In addition, the motion problem is relatively large with respect to the number of outputs, where there are hundreds of outputs (between 500-700 outputs) to predict for a single problem. Therefore, the aforementioned necessities in motion problems lead to the use of tools like the ANN in this work.

This work introduces new algorithms for the design of the radial-basis network (RBN) for problems with minimal available training data. The new RBN design incorporates new training stages with approaches to facilitate proper setting of necessary network parameters. The use of training algorithms with minimal heuristics allows the new RBN design to produce results with quality that none of the competing methods have achieved. The new RBN design, called Opt_RBN, is tested on experimental and practical problems, and the results outperform those produced from standard regression and ANN models. In general, the Opt_RBN shows stable and robust performance for a given set of training cases.

When the Opt_RBN is applied on the large-scale motion prediction application, the network experiences a CPU memory issue when performing the optimization step in the training process. Therefore, new algorithms are introduced to modify some steps of the new Opt_RBN training process to address the memory issue. The modified steps should only be used for large-scale applications similar to the motion problem. The new RBN design proposes an ANN that is capable of improved learning without needing more training data. Although the new design is driven by its use with motion prediction problems, the consequent ANN design can be used with a broad range of large-scale problems in various engineering and industrial fields that experience delay issues when running computational tools that require a massive number of procedures and a great deal of CPU memory.

The results of evaluating the modified Opt_RBN design on two motion problems are promising, with relatively small errors obtained when predicting approximately 500-700 outputs. In addition, new methods for constraint implementation within the new RBN design are introduced. Moreover, the new RBN design and its associated parameters are used as a tool for simulated task analysis. This work initiates the idea that output weights (W) can be used to determine the most critical basis functions that cause the greatest reduction in the network test error. Then, the critical basis functions can specify the most significant training cases that are responsible for the proper performance achieved by the network. The inputs with the most change in value can be extracted from the basis function centers (U) in order to determine the dominant inputs. The outputs with the most change in value and their corresponding key body degrees-of-freedom for a motion task can also be specified using the training cases that are used to create the network's basis functions.

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Matsangas, Panagiotis. "A linear physiological visual-vestibular interaction model for the prediction of motion sickness incidence." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Sep%5FMatsangas.pdf.

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Thesis (M.S. in Operations Research and M.S. in Modeling, Virtual Environments and Simulation)--Naval Postgraduate School, Sept. 2004.
Thesis Advisor(s): Michael McCauley, Nita Miller. Includes bibliographical references (p. 149-162). Also available online.
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Wang, Anqi. "Prediction of Human Hand Motions based on Surface Electromyography." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78289.

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Tracking human hand motions has raised more attention due to the recent advancements of virtual reality (Rheingold, 1991) and prosthesis control (Antfolk et al., 2010). Surface electromyography (sEMG) has been the predominant method for sensing electrical activity in biomechanical studies, and has also been applied to motion tracking in recent years. While most studies focus on the classification of human hand motions within a predefined motion set, the prediction of continuous finger joint angles and wrist angles remains a challenging endeavor. In this research, a biomechanical knowledge-driven data fusion strategy is proposed to predict finger joint angles and wrist angles. This strategy combines time series data of sEMG signals and simulated muscle features, which can be extracted from a biomechanical model available in OpenSim (Delp et al., 2007). A support vector regression (SVR) model is used to firstly predict muscle features from sEMG signals and then to predict joint angles from the estimated muscle features. A set of motion data containing 10 types of motions from 12 participants was collected from an institutional review board approved experiment. A hypothesis was tested to validate whether adding the simulated muscle features would significantly improve the prediction performance. The study indicates that the biomechanical knowledge-driven data fusion strategy will improve the prediction of new types of human hand motions. The results indicate that the proposed strategy significantly outperforms the benchmark date-driven model especially when the users were performing unknown types of motions from the model training stage. The proposed model provides a possible approach to integrate the simulation models and data fusion models in human factors and ergonomics.
Master of Science
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Kelling, Nicholas J. "An investigation of human capability to predict the future location of objects in motion." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/28103.

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Thesis (M. S.)--Psychology, Georgia Institute of Technology, 2009.
Committee Chair: Dr. Gregory M. Corso; Committee Member: Dr. Arthur D. Fisk; Committee Member: Dr. Bruce Walker; Committee Member: Dr. Lawrence R. James; Committee Member: Dr. Paul Corballis; Committee Member: Dr. Robert Gregor
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Fan, Zheyu Jerry. "Kalman Filter Based Approach : Real-time Control-based Human Motion Prediction in Teleoperation." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189210.

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This work is to investigate the performance of two Kalman Filter Algorithms, namely Linear Kalman Filter and Extended Kalman Filter on control-based human motion prediction in a real-time teleoperation. The Kalman Filter Algorithm has been widely used in research areas of motion tracking and GPS-navigation. However, the potential of human motion prediction by utilizing this algorithm is rarely being mentioned. Combine with the known issue - the delay issue in today’s teleoperation services, the author decided to build a prototype of simple teleoperation model based on the Kalman Filter Algorithm with the aim of eliminated the unsynchronization between the user’s inputs and the visual frames, where all the data were transferred over the network. In the first part of the thesis, two types of Kalman Filter Algorithm are applied on the prototype to predict the movement of the robotic arm based on the user’s motion applied on a Haptic Device. The comparisons in performance among the Kalman Filters have also been focused. In the second part, the thesis focuses on optimizing the motion prediction which based on the results of Kalman filtering by using the smoothing algorithm. The last part of the thesis examines the limitation of the prototype, such as how much the delays are accepted and how fast the movement speed of the Phantom Haptic can be, to still be able to obtain reasonable predations with acceptable error rate.   The results show that the Extended Kalman Filter has achieved more advantages in motion prediction than the Linear Kalman Filter during the experiments. The unsynchronization issue has been effectively improved by applying the Kalman Filter Algorithm on both state and measurement models when the latency is set to below 200 milliseconds. The additional smoothing algorithm further increases the accuracy. More important, it also solves shaking issue on the visual frames on robotic arm which is caused by the wavy property of the Kalman Filter Algorithm. Furthermore, the optimization method effectively synchronizes the timing when robotic arm touches the interactable object in the prediction.   The method which is utilized in this research can be a good reference for the future researches in control-based human motion tracking and prediction.
Detta arbete fokuserar på att undersöka prestandan hos två Kalman Filter Algoritmer, nämligen Linear Kalman Filter och Extended Kalman Filter som används i realtids uppskattningar av kontrollbaserad mänsklig rörelse i teleoperationen. Dessa Kalman Filter Algoritmer har används i stor utsträckning forskningsområden i rörelsespårning och GPS-navigering. Emellertid är potentialen i uppskattning av mänsklig rörelse genom att utnyttja denna algoritm sällan nämnas. Genom att kombinera med det kända problemet – fördröjningsproblem i dagens teleoperation tjänster beslutar författaren att bygga en prototyp av en enkel teleoperation modell vilket är baserad på Kalman Filter algoritmen i syftet att eliminera icke-synkronisering mellan användarens inmatningssignaler och visuella information, där alla data överfördes via nätverket. I den första delen av avhandlingen appliceras både Kalman Filter Algoritmer på prototypen för att uppskatta rörelsen av robotarmen baserat på användarens rörelse som anbringas på en haptik enhet. Jämförelserna i prestandan bland de Kalman Filter Algoritmerna har också fokuserats. I den andra delen fokuserar avhandlingen på att optimera uppskattningar av rörelsen som baserat på resultaten av Kalman-filtrering med hjälp av en utjämningsalgoritm. Den sista delen av avhandlingen undersökes begräsning av prototypen, som till exempel hur mycket fördröjningar accepteras och hur snabbt den haptik enheten kan vara, för att kunna erhålla skäliga uppskattningar med acceptabel felfrekvens.   Resultaten visar att den Extended Kalman Filter har bättre prestandan i rörelse uppskattningarna än den Linear Kalman Filter under experimenten. Det icke-synkroniseringsproblemet har förbättrats genom att tillämpa de Kalman Filter Algoritmerna på både statliga och värderingsmodeller när latensen är inställd på under 200 millisekunder. Den extra utjämningsalgoritmen ökar ytterligare noggrannheten. Denna algoritm löser också det skakande problem hos de visuella bilder på robotarmen som orsakas av den vågiga egenskapen hos Kalman Filter Algoritmen. Dessutom effektivt synkroniserar den optimeringsmetoden tidpunkten när robotarmen berör objekten i uppskattningarna.   Den metod som används i denna forskning kan vara en god referens för framtida undersökningar i kontrollbaserad rörelse- spåning och uppskattning.
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Verveniotis, Christos S. "Prediction of motion sickness on high-speed passenger vessels : a human-oriented approach." Thesis, University of Strathclyde, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.415297.

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Boonpratatong, Amaraporn. "Motion prediction and dynamic stability analysis of human walking : the effect of leg property." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/motion-prediction-and-dynamic-stability-analysis-of-human-walking-the-effect-of-leg-property(f36922af-1231-4dac-a92f-a16cbed8d701).html.

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The objective of this thesis is to develop and validate a computational framework based on mathematical models for the motion prediction and dynamic stability quantification of human walking, which can differentiate the dynamic stability of human walking with different mechanical properties of the leg. Firstly, a large measurement database of human walking motion was created. It contains walking measurement data of 8 subjects on 3 self-selected walking speeds, which 10 trials were recorded at each walking speed. The motion of whole-body centre of mass and the leg were calculated from the kinetic-kinematic measurement data. The fundamentals of leg property have been presented, and the parameters of leg property were extracted from the measurement data of human walking where the effects of walking speed and condition of foot-ground contact were investigated. Three different leg property definitions comprising linear axial elastic leg property, nonlinear axial elastic leg property and linear axial-tangential elastic leg property were used to extracted leg property parameters. The concept of posture-dependent leg property has been proposed, and the leg property parameters were extracted from the measurement data of human walking motion where the effects of walking speed and condition of foot-ground contact were also investigated. The compliant leg model with axial elastic property (CAE) was used for the dynamic stability analysis of human walking with linear and nonlinear axial elastic leg property. The compliant leg model with axial and tangential elastic property (CATE) was used for that with linear axial-tangential elastic leg property. The posture - dependent elastic leg model (PDE) was used for that with posture-dependent leg property. It was found that, with linear axial elastic leg property, the global stability of human walking improves with the bigger touchdown contact angle. The average leg property obtained from the measurement data of all participants allows the maximum global stability of human walking. With nonlinear axial elastic leg property, the global stability decreases with the stronger nonlinearity of leg stiffness. The incorporation of the tangential elasticity improves the global stability and shifts the stable walking velocity close to that of human walking at self-selected low speed (1.1-1.25 m/s).By the PDE model, the human walking motions were better predicted than by the CATE model. The effective range of walking prediction was enlarged to 1.12 – 1.8 m/s. However, represented by PDE model, only 1-2 walking steps can be achieved. In addition, the profiles of mechanical energies represented by the PDE model are different from that of the orbital stable walking represented by CATE model. Finally, the minimal requirements of the human walking measurements and the flexibility of simple walking models with deliberate leg property definitions allow the computational framework to be applicable in the dynamic stability analysis of the walking motion with a wide variety of mechanical property of the leg.
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Bataineh, Mohammad Hindi. "Artificial neural network for studying human performance." Thesis, University of Iowa, 2012. https://ir.uiowa.edu/etd/3259.

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The vast majority of products and processes in industry and academia require human interaction. Thus, digital human models (DHMs) are becoming critical for improved designs, injury prevention, and a better understanding of human behavior. Although many capabilities in the DHM field continue to mature, there are still many opportunities for improvement, especially with respect to posture- and motion-prediction. Thus, this thesis investigates the use of artificial neural network (ANN) for improving predictive capabilities and for better understanding how and why human behave the way they do. With respect to motion prediction, one of the most challenging opportunities for improvement concerns computation speed. Especially, when considering dynamic motion prediction, the underlying optimization problems can be large and computationally complex. Even though the current optimization-based tools for predicting human posture are relatively fast and accurate and thus do not require as much improvement, posture prediction in general is a more tractable problem than motion prediction and can provide a test bead that can shed light on potential issues with motion prediction. Thus, we investigate the use of ANN with posture prediction in order to discover potential issues. In addition, directly using ANN with posture prediction provides a preliminary step towards using ANN to predict the most appropriate combination of performance measures (PMs) - what drives human behavior. The PMs, which are the cost functions that are minimized in the posture prediction problem, are typically selected manually depending on the task. This is perhaps the most significant impediment when using posture prediction. How does the user know which PMs should be used? Neural networks provide tools for solving this problem. This thesis hypothesizes that the ANN can be trained to predict human motion quickly and accurately, to predict human posture (while considering external forces), and to determine the most appropriate combination of PM(s) for posture prediction. Such capabilities will in turn provide a new tool for studying human behavior. Based on initial experimentation, the general regression neural network (GRNN) was found to be the most effective type of ANN for DHM applications. A semi-automated methodology was developed to ease network construction, training and testing processes, and network parameters. This in turn facilitates use with DHM applications. With regards to motion prediction, use of ANN was successful. The results showed that the calculation time was reduced from 1 to 40 minutes, to a fraction of a second without reducing accuracy. With regards to posture prediction, ANN was again found to be effective. However, potential issues with certain motion-prediction tasks were discovered and shed light on necessary future development with ANNs. Finally, a decision engine was developed using GRNN for automatically selecting four human PMs, and was shown to be very effective. In order to train this new approach, a novel optimization formulation was used to extract PM weights from pre-existing motion-capture data. Eventually, this work will lead to automatically and realistically driving predictive DHMs in a general virtual environment.
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Books on the topic "Human Motion Prediction"

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Herzog, Walter. Individual muscle force prediction in athletic movements. 1986.

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Herzog, Walter. Individual muscle force prediction in athletic movements. 1985.

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Wu, Min. Hybrid Physical and Data-Driven Approach to Motion Prediction and Control in Human-Robot Collaboration. Logos Verlag Berlin, 2022.

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Human Motion Simulation: Predictive Dynamics. Elsevier Science & Technology Books, 2013.

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Abdel-Malek, Karim, and Jasbir Singh Arora. Human Motion Simulation: Predictive Dynamics. Elsevier Science & Technology Books, 2013.

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Cullen, Christopher. Introduction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198733119.003.0001.

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The narrative I construct in this book lays emphasis on technical practice in observation, instrumentation and calculation, and the steady accumulation of data over many years—but it centres on the activity of the individual human beings who observed the heavens, recorded what they saw, and made calculations to analyse and eventually make predictions about the motions of the celestial bodies. Some of these people had official posts that gave them responsibility for work of this kind; others held official rank without such responsibilities, but still played a major role in technical discussions about celestial phenomena. A few others held no official rank at all, but showed themselves well capable of talking and writing about the heavens at an expert level. It is these individuals, their observations, their calculations and the words they left to us that provide the narrative thread that runs through this work....
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Butz, Martin V., and Esther F. Kutter. How the Mind Comes into Being. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198739692.001.0001.

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For more than 2000 years Greek philosophers have thought about the puzzling introspectively assessed dichotomy between our physical bodies and our seemingly non-physical minds. How is it that we can think highly abstract thoughts, seemingly fully detached from actual, physical reality? Despite the obvious interactions between mind and body (we get tired, we are hungry, we stay up late despite being tired, etc.), until today it remains puzzling how our mind controls our body, and vice versa, how our body shapes our mind. Despite a big movement towards embodied cognitive science over the last 20 years or so, introductory books with a functional and computational perspective on how human thought and language capabilities may actually have come about – and are coming about over and over again – are missing. This book fills that gap. Starting with a historical background on traditional cognitive science and resulting fundamental challenges that have not been resolved, embodied cognitive science is introduced and its implications for how human minds have come and continue to come into being are detailed. In particular, the book shows that evolution has produced biological bodies that provide “morphologically intelligent” structures, which foster the development of suitable behavioral and cognitive capabilities. While these capabilities can be modified and optimized given positive and negative reward as feedback, to reach abstract cognitive capabilities, evolution has furthermore produced particular anticipatory control-oriented mechanisms, which cause the development of particular types of predictive encodings, modularizations, and abstractions. Coupled with an embodied motivational system, versatile, goal-directed, self-motivated behavior, learning becomes possible. These lines of thought are introduced and detailed from interdisciplinary, evolutionary, ontogenetic, reinforcement learning, and anticipatory predictive encoding perspectives in the first part of the book. A short excursus then provides an introduction to neuroscience, including general knowledge about brain anatomy, and basic neural and brain functionality, as well as the main research methodologies. With reference to this knowledge, the subsequent chapters then focus on how the human brain manages to develop abstract thought and language. Sensory systems, motor systems, and their predictive, control-oriented interactions are detailed from a functional and computational perspective. Bayesian information processing is introduced along these lines as are generative models. Moreover, it is shown how particular modularizations can develop. When control and attention come into play, these structures develop also dependent on the available motor capabilities. Vice versa, the development of more versatile motor capabilities depends on structural development. Event-oriented abstractions enable conceptualizations and behavioral compositions, paving the path towards abstract thought and language. Also evolutionary drives towards social interactions play a crucial role. Based on the developing sensorimotor- and socially-grounded structures, the human mind becomes language ready. The development of language in each human child then further facilitates the self-motivated generation of abstract, compositional, highly flexible thought about the present, past, and future, as well as about others. In conclusion, the book gives an overview over how the human mind comes into being – sketching out a developmental pathway towards the mastery of abstract and reflective thought, while detailing the critical body and neural functionalities, and computational mechanisms, which enable this development.
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Cullen, Christopher. Heavenly Numbers. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198733119.001.0001.

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This book is a history of the development of mathematical astronomy in China, from the late third century BCE, to the early third century CE—a period often referred to as ‘early imperial China’. It narrates the changes in ways of understanding the movements of the heavens and the heavenly bodies that took place during those four and a half centuries, and tells the stories of the institutions and individuals involved in those changes. It gives clear explanations of technical practice in observation, instrumentation and calculation, and the steady accumulation of data over many years—but it centres on the activity of the individual human beings who observed the heavens, recorded what they saw, and made calculations to analyse and eventually make predictions about the motions of the celestial bodies. It is these individuals, their observations, their calculations and the words they left to us that provide the narrative thread that runs through this work. Throughout the book, the author gives clear translations of original material that allow the reader direct access to what the people in this book said about themselves and what they tried to do. This book is designed to be accessible to a broad readership interested in the history of science, the history of China and the comparative history of ancient cultures, while still being useful to specialists in the history of astronomy.
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Book chapters on the topic "Human Motion Prediction"

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Gui, Liang-Yan, Yu-Xiong Wang, Xiaodan Liang, and José M. F. Moura. "Adversarial Geometry-Aware Human Motion Prediction." In Computer Vision – ECCV 2018, 823–42. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01225-0_48.

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Mao, Wei, Miaomiao Liu, and Mathieu Salzmann. "History Repeats Itself: Human Motion Prediction via Motion Attention." In Computer Vision – ECCV 2020, 474–89. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58568-6_28.

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Lee, Suk Jin, and Yuichi Motai. "Phantom: Prediction of Human Motion with Distributed Body Sensors." In Prediction and Classification of Respiratory Motion, 39–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41509-8_3.

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Borish, Michael, and Benjamin Lok. "Utilizing Unsupervised Crowdsourcing to Develop a Machine Learning Model for Virtual Human Animation Prediction." In Handbook of Human Motion, 2289–306. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-14418-4_21.

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Borish, Michael, and Benjamin Lok. "Utilizing Unsupervised Crowdsourcing to Develop a Machine Learning Model for Virtual Human Animation Prediction." In Handbook of Human Motion, 1–18. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30808-1_21-1.

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Chao, Xianjin, Yanrui Bin, Wenqing Chu, Xuan Cao, Yanhao Ge, Chengjie Wang, Jilin Li, Feiyue Huang, and Howard Leung. "Adversarial Refinement Network for Human Motion Prediction." In Computer Vision – ACCV 2020, 454–69. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69532-3_28.

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Li, Yanan, and Chenguang Yang. "A Hybrid Human Motion Prediction Approach for Human-Robot Collaboration." In Advances in Intelligent Systems and Computing, 81–91. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29933-0_7.

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Gao, Robert X., Lihui Wang, Peng Wang, Jianjing Zhang, and Hongyi Liu. "Human Motion Recognition and Prediction for Robot Control." In Advanced Human-Robot Collaboration in Manufacturing, 261–82. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69178-3_11.

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Cao, Zhe, Hang Gao, Karttikeya Mangalam, Qi-Zhi Cai, Minh Vo, and Jitendra Malik. "Long-Term Human Motion Prediction with Scene Context." In Computer Vision – ECCV 2020, 387–404. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58452-8_23.

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Cai, Yujun, Lin Huang, Yiwei Wang, Tat-Jen Cham, Jianfei Cai, Junsong Yuan, Jun Liu, et al. "Learning Progressive Joint Propagation for Human Motion Prediction." In Computer Vision – ECCV 2020, 226–42. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58571-6_14.

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Conference papers on the topic "Human Motion Prediction"

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Ling, Hejing, Guoliang Liu, Liujuan Zhu, Bin Huang, Fei Lu, Hao Wu, Guohui Tian, and Ze Ji. "Motion Planning Combines Human Motion Prediction for Human-Robot Cooperation." In 2022 12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). IEEE, 2022. http://dx.doi.org/10.1109/cyber55403.2022.9907516.

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Corona, Enric, Albert Pumarola, Guillem Alenya, and Francesc Moreno-Noguer. "Context-Aware Human Motion Prediction." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.00702.

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Liao, Hao-yu, Minghui Zheng, Boyi Hu, and Sara Behdad. "Human Hand Motion Prediction in Disassembly Operations." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-89967.

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Abstract The remanufacturing workforce can benefit from the capabilities of robotic technology, where robots can alleviate the labor-intensive nature of disassembly operations and help with handling toxic and hazardous materials. However, operators’ safety is an important aspect of human-robot collaboration in disassembly operations. This study focuses on predicting human hand motion to provide advanced information to disassembly robots when collaborating with humans. A prediction framework is proposed, which consists of two deep learning models, including convolutional long short-term memory (ConvLSTM) and You Only Look Once (YOLO). ConvLSTM forecasts the next-frame image using images from the disassembly process, and then the YOLO model identifies the human hand object on the predicted image resulting from ConvLSTM. The disassembly images collected from four desktop computers are used to train the ConvLSTM and YOLO. The results reveal that the combined framework of ConvLSTM and YOLO performs well in predicting human hand motion and locating the hand object. The outcomes highlight the need for developing deep learning models capable of recognizing human motion when working with different designs as often remanufacturing workforce have to deal with a wide range of products from different brands, models, and conditions.
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Zang, Chuanqi, Mingtao Pei, and Yu Kong. "Few-shot Human Motion Prediction via Learning Novel Motion Dynamics." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/118.

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Human motion prediction is a task where we anticipate future motion based on past observation. Previous approaches rely on the access to large datasets of skeleton data, and thus are difficult to be generalized to novel motion dynamics with limited training data. In our work, we propose a novel approach named Motion Prediction Network (MoPredNet) for few-short human motion prediction. MoPredNet can be adapted to predicting new motion dynamics using limited data, and it elegantly captures long-term dependency in motion dynamics. Specifically, MoPredNet dynamically selects the most informative poses in the streaming motion data as masked poses. In addition, MoPredNet improves its encoding capability of motion dynamics by adaptively learning spatio-temporal structure from the observed poses and masked poses. We also propose to adapt MoPredNet to novel motion dynamics based on accumulated motion experiences and limited novel motion dynamics data. Experimental results show that our method achieves better performance over state-of-the-art methods in motion prediction.
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Lasota, Przemyslaw A., and Julie A. Shah. "A multiple-predictor approach to human motion prediction." In 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017. http://dx.doi.org/10.1109/icra.2017.7989265.

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Wang, Ziyou, Shengpeng Liu, and Yan Xu. "Human motion prediction based on hybrid motion model." In 2017 IEEE International Conference on Information and Automation (ICIA). IEEE, 2017. http://dx.doi.org/10.1109/icinfa.2017.8079038.

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Wang, Ziyou, Shengpeng Liu, and Yan Xu. "Human Motion Prediction Based on Hybrid Motion Model." In 2018 IEEE International Conference on Information and Automation (ICIA). IEEE, 2018. http://dx.doi.org/10.1109/icinfa.2018.8812358.

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Faraway, Julian J. "Data-Based Motion Prediction." In Digital Human Modeling for Design and Engineering Conference and Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2003. http://dx.doi.org/10.4271/2003-01-2229.

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Wang, Junyi, and Xinyu Su. "Enriching Intention of Human Motion Prediction." In ICCDE 2020: 2020 The 6th International Conference on Computing and Data Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3379247.3379295.

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Maeda, Takahiro, and Norimichi Ukita. "Data Augmentation for Human Motion Prediction." In 2021 17th International Conference on Machine Vision and Applications (MVA). IEEE, 2021. http://dx.doi.org/10.23919/mva51890.2021.9511368.

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