Journal articles: 'Human Motion Prediction'

1

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

14

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

15

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.

Full text

Abstract:

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)

APA, Harvard, Vancouver, ISO, and other styles

16

Liang, Jifei. "Human Boxing Motion Prediction Using Neural Networks." OA Journal of Computer Networking 1, no. 2 (November 29, 2022): 54–60. http://dx.doi.org/10.26855/oajcn.2022.09.007.

Full text

Abstract:

Numerous thoughts that were previously deemed inconceivable have become a reality as a result of decades of technical progress and improvement. While flying automobiles are still in the far future, artificial intelligence that can predict your next move is rapidly approaching. Human motion prediction is a relatively new area of active research that is interesting for it’s potential of improving robot’s and other machinery’s ability to work with human, such as passing objects to human, and avoiding crash into human, etc. This thesis focuses on predicting human boxing moves based on RGB visual input as an artificially intelligent boxing trainer with the help of recurrent neural networks (RNNs). I study and compares the performance of six distinct neural network architectures. I have method 1, which includes four model architectures taking 3D joint data as input, and method 2, which includes two architectures that take RGB image as input. Based on the results of all my research, I have discovered the most effective and efficient architecture for scenarios with sparse data based on the outcome of my study.

APA, Harvard, Vancouver, ISO, and other styles

17

Li, Shiqi, Haipeng Wang, Shuai Zhang, Shuze Wang, and Ke Han. "Human Motion Trajectory Prediction in Human-Robot Collaborative Tasks." IOP Conference Series: Materials Science and Engineering 646 (October 17, 2019): 012067. http://dx.doi.org/10.1088/1757-899x/646/1/012067.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Elfring, Jos, René van de Molengraft, and Maarten Steinbuch. "Learning intentions for improved human motion prediction." Robotics and Autonomous Systems 62, no. 4 (April 2014): 591–602. http://dx.doi.org/10.1016/j.robot.2014.01.003.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Sang, Hai-Feng, Zi-Zhen Chen, and Da-Kuo He. "Human Motion prediction based on attention mechanism." Multimedia Tools and Applications 79, no. 9-10 (December 6, 2019): 5529–44. http://dx.doi.org/10.1007/s11042-019-08269-7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Bataineh, Mohammad, Timothy Marler, Karim Abdel-Malek, and Jasbir Arora. "Neural network for dynamic human motion prediction." Expert Systems with Applications 48 (April 2016): 26–34. http://dx.doi.org/10.1016/j.eswa.2015.11.020.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Liang, Jifei. "Human Boxing Motion Prediction Using Neural Networks." OA Journal of Computer Networking 1, no. 2 (November 29, 2022): 42–48. http://dx.doi.org/10.26855/oajcn.2022.09.006.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Yunus, Andi Prademon, Nobu C. Shirai, Kento Morita, and Tetsushi Wakabayashi. "Comparison of RNN-LSTM and Kalman Filter Based Time Series Human Motion Prediction." Journal of Physics: Conference Series 2319, no. 1 (August 1, 2022): 012034. http://dx.doi.org/10.1088/1742-6596/2319/1/012034.

Full text

Abstract:

Abstract Machine-human and machine-machine interaction is inevitable and needs to be considered apart from human-machine interaction. For the safety of machine interaction, the system is required to prevent any unexpected accident. This paper proposed a system that can forecast human motion to know the movement direction for one second. This research was conducted by using an RGB camera as a reliable alternative. The feature extraction process has been done by OpenPose to obtain the coordinate of human body parts in the frame. Then the coordinate data is converted to the movement data containing the distance and direction in the key points to the next frame for the input in the prediction method. This research aims for the human motion prediction using these methods to compare the performance on the human motion data and realize the prediction of human motion. Mainly, Kalman Filter shows more positive results than RNN-LSTM as the prediction method. Movement like hand gestures is more effortless than motions like hand gestures and steps to the left side. The validity of the system based on the RGB camera with unstable data has been confirmed based on the results.

APA, Harvard, Vancouver, ISO, and other styles

23

Javeed, Madiha, Mohammad Shorfuzzaman, Nawal Alsufyani, Samia Allaoua Chelloug, Ahmad Jalal, and Jeongmin Park. "Physical human locomotion prediction using manifold regularization." PeerJ Computer Science 8 (October 12, 2022): e1105. http://dx.doi.org/10.7717/peerj-cs.1105.

Full text

Abstract:

Human locomotion is an imperative topic to be conversed among researchers. Predicting the human motion using multiple techniques and algorithms has always been a motivating subject matter. For this, different methods have shown the ability of recognizing simple motion patterns. However, predicting the dynamics for complex locomotion patterns is still immature. Therefore, this article proposes unique methods including the calibration-based filter algorithm and kinematic-static patterns identification for predicting those complex activities from fused signals. Different types of signals are extracted from benchmarked datasets and pre-processed using a novel calibration-based filter for inertial signals along with a Bessel filter for physiological signals. Next, sliding overlapped windows are utilized to get motion patterns defined over time. Then, polynomial probability distribution is suggested to decide the motion patterns natures. For features extraction based kinematic-static patterns, time and probability domain features are extracted over physical action dataset (PAD) and growing old together validation (GOTOV) dataset. Further, the features are optimized using quadratic discriminant analysis and orthogonal fuzzy neighborhood discriminant analysis techniques. Manifold regularization algorithms have also been applied to assess the performance of proposed prediction system. For the physical action dataset, we achieved an accuracy rate of 82.50% for patterned signals. While, the GOTOV dataset, we achieved an accuracy rate of 81.90%. As a result, the proposed system outdid when compared to the other state-of-the-art models in literature.

APA, Harvard, Vancouver, ISO, and other styles

24

Chu, Zhesen, and Min Li. "A Specific Algorithm Based on Motion Direction Prediction." Complexity 2021 (February 15, 2021): 1–11. http://dx.doi.org/10.1155/2021/6678596.

Full text

Abstract:

In this paper, we study the estimation of motion direction prediction for fast motion and propose a threshold-based human target detection algorithm using motion vectors and other data as human target feature information. The motion vectors are partitioned into regions by normalization to form a motion vector field, which is then preprocessed, and then the human body target is detected through its motion vector region block-temporal correlation to detect the human body motion target. The experimental results show that the algorithm is effective in detecting human motion targets in videos with the camera relatively stationary. The algorithm predicts the human body position in the reference frame of the current frame in the video by forward mapping the motion vector of the current frame, then uses the motion vector direction angle histogram as a matching feature, and combines it with a region matching strategy to track the human body target in the predicted region, thus realizing the human body target tracking effect. The algorithm is experimentally proven to effectively track human motion targets in videos with relatively static backgrounds. To address the problem of sample diversity and lack of quantity in a multitarget tracking environment, a generative model based on the conditional variational self-encoder conditional generation of adversarial networks is proposed, and the performance of the generative model is verified using pedestrian reidentification and other datasets, and the experimental results show that the method can take advantage of the advantages of both models to improve the quality of the generated results.

APA, Harvard, Vancouver, ISO, and other styles

25

Park, Jae Sung, Chonhyon Park, and Dinesh Manocha. "I-Planner: Intention-aware motion planning using learning-based human motion prediction." International Journal of Robotics Research 38, no. 1 (November 30, 2018): 23–39. http://dx.doi.org/10.1177/0278364918812981.

Full text

Abstract:

We present a motion planning algorithm to compute collision-free and smooth trajectories for high-degree-of-freedom (high-DOF) robots interacting with humans in a shared workspace. Our approach uses offline learning of human actions along with temporal coherence to predict the human actions. Our intention-aware online planning algorithm uses the learned database to compute a reliable trajectory based on the predicted actions. We represent the predicted human motion using a Gaussian distribution and compute tight upper bounds on collision probabilities for safe motion planning. We also describe novel techniques to account for noise in human motion prediction. We highlight the performance of our planning algorithm in complex simulated scenarios and real-world benchmarks with 7-DOF robot arms operating in a workspace with a human performing complex tasks. We demonstrate the benefits of our intention-aware planner in terms of computing safe trajectories in such uncertain environments.

APA, Harvard, Vancouver, ISO, and other styles

26

Xia, Xiaolu, Tianyu Zhou, Jing Du, and Nan Li. "Human motion prediction for intelligent construction: A review." Automation in Construction 142 (October 2022): 104497. http://dx.doi.org/10.1016/j.autcon.2022.104497.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

OKADA, Masanori, and Hideyoshi Yanagisawa. "Modeling Human Prediction of Motion of Mobile Robot." Proceedings of Design & Systems Conference 2021.31 (2021): 2407. http://dx.doi.org/10.1299/jsmedsd.2021.31.2407.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Bajcsy, Andrea, Somil Bansal, Ellis Ratner, Claire J. Tomlin, and Anca D. Dragan. "A Robust Control Framework for Human Motion Prediction." IEEE Robotics and Automation Letters 6, no. 1 (January 2021): 24–31. http://dx.doi.org/10.1109/lra.2020.3028049.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Ferrer, Gonzalo, and Alberto Sanfeliu. "Bayesian Human Motion Intentionality Prediction in urban environments." Pattern Recognition Letters 44 (July 2014): 134–40. http://dx.doi.org/10.1016/j.patrec.2013.08.013.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Li, Yanran, Zhao Wang, Xiaosong Yang, Meili Wang, Sebastian Iulian Poiana, Ehtzaz Chaudhry, and Jianjun Zhang. "Efficient convolutional hierarchical autoencoder for human motion prediction." Visual Computer 35, no. 6-8 (May 11, 2019): 1143–56. http://dx.doi.org/10.1007/s00371-019-01692-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

BEUTTER, BRENT R., and LELAND S. STONE. "Motion coherence affects human perception and pursuit similarly." Visual Neuroscience 17, no. 1 (January 2000): 139–53. http://dx.doi.org/10.1017/s0952523800171147.

Full text

Abstract:

Pursuit and perception both require accurate information about the motion of objects. Recovering the motion of objects by integrating the motion of their components is a difficult visual task. Successful integration produces coherent global object motion, while a failure to integrate leaves the incoherent local motions of the components unlinked. We compared the ability of perception and pursuit to perform motion integration by measuring direction judgments and the concomitant eye-movement responses to line-figure parallelograms moving behind stationary rectangular apertures. The apertures were constructed such that only the line segments corresponding to the parallelogram's sides were visible; thus, recovering global motion required the integration of the local segment motion. We investigated several potential motion-integration rules by using stimuli with different object, vector-average, and line-segment terminator-motion directions. We used an oculometric decision rule to directly compare direction discrimination for pursuit and perception. For visible apertures, the percept was a coherent object, and both the pursuit and perceptual performance were close to the object-motion prediction. For invisible apertures, the percept was incoherently moving segments, and both the pursuit and perceptual performance were close to the terminator-motion prediction. Furthermore, both psychometric and oculometric direction thresholds were much higher for invisible apertures than for visible apertures. We constructed a model in which both perception and pursuit are driven by a shared motion-processing stage, with perception having an additional input from an independent static-processing stage. Model simulations were consistent with our perceptual and oculomotor data. Based on these results, we propose the use of pursuit as an objective and continuous measure of perceptual coherence. Our results support the view that pursuit and perception share a common motion-integration stage, perhaps within areas MT or MST.

APA, Harvard, Vancouver, ISO, and other styles

32

Shi, Xiaodan, Xiaowei Shao, Zhiling Guo, Guangming Wu, Haoran Zhang, and Ryosuke Shibasaki. "Pedestrian Trajectory Prediction in Extremely Crowded Scenarios." Sensors 19, no. 5 (March 11, 2019): 1223. http://dx.doi.org/10.3390/s19051223.

Full text

Abstract:

Pedestrian trajectory prediction under crowded circumstances is a challenging problem owing to human interaction and the complexity of the trajectory pattern. Various methods have been proposed for solving this problem, ranging from traditional Bayesian analysis to Social Force model and deep learning methods. However, most existing models heavily depend on specific scenarios because the trajectory model is constructed in absolute coordinates even though the motion trajectory as well as human interaction are in relative motion. In this study, a novel trajectory prediction model is proposed to capture the relative motion of pedestrians in extremely crowded scenarios. Trajectory sequences and human interaction are first represented with relative motion and then integrated to our model to predict pedestrians’ trajectories. The proposed model is based on Long Short Term Memory (LSTM) structure and consists of an encoder and a decoder which are trained by truncated back propagation. In addition, an anisotropic neighborhood setting is proposed instead of traditional neighborhood analysis. The proposed approach is validated using trajectory data acquired at an extremely crowded train station in Tokyo, Japan. The trajectory prediction experiments demonstrated that the proposed method outperforms existing methods and is stable for predictions of varying length even when the model is trained with a controlled short trajectory sequence.

APA, Harvard, Vancouver, ISO, and other styles

33

XIANG, YUJIANG, JASBIR S. ARORA, and KARIM ABDEL-MALEK. "3D HUMAN LIFTING MOTION PREDICTION WITH DIFFERENT PERFORMANCE MEASURES." International Journal of Humanoid Robotics 09, no. 02 (June 2012): 1250012. http://dx.doi.org/10.1142/s0219843612500120.

Full text

Abstract:

This paper presents an optimization-based method for predicting a human dynamic lifting task. The three-dimensional digital human skeletal model has 55 degrees of freedom. Lifting motion is generated by minimizing an objective function (human performance measure) subjected to basic physical and kinematical constraints. Four objective functions are investigated in the formulation: the dynamic effort, the balance criterion, the maximum shear force at spine joint and the maximum pressure force at spine joint. The simulation results show that various human performance measures predict different lifting strategies: the balance and shear force performance measures predict back-lifting motion and the dynamic effort and pressure force performance measures generate squat-lifting motion. In addition, the effects of box locations on the lifting strategies are also studied. All kinematics and kinetic data are successfully predicted for the lifting motion by using the predictive dynamics algorithm and the optimal solution was obtained in about one minute.

APA, Harvard, Vancouver, ISO, and other styles

34

Chung, Hyun-Joon, Yujiang Xiang, Jasbir S. Arora, and Karim Abdel-Malek. "Optimization-based dynamic 3D human running prediction: effects of foot location and orientation." Robotica 33, no. 2 (March 4, 2014): 413–35. http://dx.doi.org/10.1017/s0263574714000253.

Full text

Abstract:

SUMMARYThis paper presents optimization-based dynamic three-dimensional (3D) human running prediction. A predictive dynamics method is used to formulate the running problem, and normal running is formulated as a symmetric and cyclic motion. In addition, a slow jog along curved paths has been formulated. It is a non-symmetric running motion, so a stride formulation has been used. The dynamic effort and impulse are used as the performance measure, and the upper body yawing moment is also included in the performance measure. The joint angle profiles and joint torque profiles are calculated for the full-body human model, and the ground reaction force is determined. The effects of foot location and orientation on the running motion prediction are simulated and studied. Simulation results from this methodology show good correlation with experimental data obtained from human subjects.

APA, Harvard, Vancouver, ISO, and other styles

35

Ding, Zhen, Chifu Yang, Zhipeng Wang, Xunfeng Yin, and Feng Jiang. "Online Adaptive Prediction of Human Motion Intention Based on sEMG." Sensors 21, no. 8 (April 20, 2021): 2882. http://dx.doi.org/10.3390/s21082882.

Full text

Abstract:

Accurate and reliable motion intention perception and prediction are keys to the exoskeleton control system. In this paper, a motion intention prediction algorithm based on sEMG signal is proposed to predict joint angle and heel strike time in advance. To ensure the accuracy and reliability of the prediction algorithm, the proposed method designs the sEMG feature extraction network and the online adaptation network. The feature extraction utilizes the convolution autoencoder network combined with muscle synergy characteristics to get the high-compression sEMG feature to aid motion prediction. The adaptation network ensures the proposed prediction method can still maintain a certain prediction accuracy even the sEMG signals distribution changes by adjusting some parameters of the feature extraction network and the prediction network online. Ten subjects were recruited to collect surface EMG data from nine muscles on the treadmill. The proposed prediction algorithm can predict the knee angle 101.25 ms in advance with 2.36 degrees accuracy. The proposed prediction algorithm also can predict the occurrence time of initial contact 236±9 ms in advance. Meanwhile, the proposed feature extraction method can achieve 90.71±3.42% accuracy of sEMG reconstruction and can guarantee 73.70±5.01% accuracy even when the distribution of sEMG is changed without any adjustment. The online adaptation network enhances the accuracy of sEMG reconstruction of CAE to 87.65±3.83% and decreases the angle prediction error from 4.03∘ to 2.36∘. The proposed method achieves effective motion prediction in advance and alleviates the influence caused by the non-stationary of sEMG.

APA, Harvard, Vancouver, ISO, and other styles

36

Li, Qinghua, Zhao Zhang, Yue You, Yaqi Mu, and Chao Feng. "Data Driven Models for Human Motion Prediction in Human-Robot Collaboration." IEEE Access 8 (2020): 227690–702. http://dx.doi.org/10.1109/access.2020.3045994.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Khawaja, Fahad Iqbal, Akira Kanazawa, Jun Kinugawa, and Kazuhiro Kosuge. "A Human-Following Motion Planning and Control Scheme for Collaborative Robots Based on Human Motion Prediction." Sensors 21, no. 24 (December 9, 2021): 8229. http://dx.doi.org/10.3390/s21248229.

Full text

Abstract:

Human–Robot Interaction (HRI) for collaborative robots has become an active research topic recently. Collaborative robots assist human workers in their tasks and improve their efficiency. However, the worker should also feel safe and comfortable while interacting with the robot. In this paper, we propose a human-following motion planning and control scheme for a collaborative robot which supplies the necessary parts and tools to a worker in an assembly process in a factory. In our proposed scheme, a 3-D sensing system is employed to measure the skeletal data of the worker. At each sampling time of the sensing system, an optimal delivery position is estimated using the real-time worker data. At the same time, the future positions of the worker are predicted as probabilistic distributions. A Model Predictive Control (MPC)-based trajectory planner is used to calculate a robot trajectory that supplies the required parts and tools to the worker and follows the predicted future positions of the worker. We have installed our proposed scheme in a collaborative robot system with a 2-DOF planar manipulator. Experimental results show that the proposed scheme enables the robot to provide anytime assistance to a worker who is moving around in the workspace while ensuring the safety and comfort of the worker.

APA, Harvard, Vancouver, ISO, and other styles

38

Tang, Jin, Jin Liu, and JianQin Yin. "A Hierarchical Static-Dynamic Encoder-Decoder Structure for 3D Human Motion Prediction with Residual CNNs." Mathematical Problems in Engineering 2020 (August 27, 2020): 1–12. http://dx.doi.org/10.1155/2020/7064910.

Full text

Abstract:

Human motion prediction aims at predicting the future poses according to the motion dynamics given by the sequence of history poses. We present a new hierarchical static-dynamic encoder-decoder structure to predict the human motion with residual CNNs. Specifically, to better mine the law of the motion, a new residual CNN-based structure, v-CMU, is proposed to encode not only the static information but also the dynamic information. Based on v-CMU, a hierarchical structure is proposed to model different correlations between the different given poses and the predicted pose. Moreover, a new loss function combining the static and dynamic information is introduced in the decoder to guide the prediction of the future poses. Our framework features two-folds: (1) more effective dynamics mined due to the fusion of information of the poses and the dynamic information between poses and the hierarchical structure; (2) better decoding or prediction performance, thanks to the mid-level supervision introduced by the new loss function considering both the static and dynamic losses. Extensive experiments show that our algorithm can achieve state-of-the-art performance on the challenging G3D and FNTU datasets. The code is available at https://github.com/liujin0/SDnet.

APA, Harvard, Vancouver, ISO, and other styles

39

Zheng, Pu, Pierre-Brice Wieber, Junaid Baber, and Olivier Aycard. "Human Arm Motion Prediction for Collision Avoidance in a Shared Workspace." Sensors 22, no. 18 (September 14, 2022): 6951. http://dx.doi.org/10.3390/s22186951.

Full text

Abstract:

Industry 4.0 transforms classical industrial systems into more human-centric and digitized systems. Close human–robot collaboration is becoming more frequent, which means security and efficiency issues need to be carefully considered. In this paper, we propose to equip robots with exteroceptive sensors and online motion generation so that the robot is able to perceive and predict human trajectories and react to the motion of the human in order to reduce the occurrence of the collisions. The dataset for training is generated in a real environment in which a human and a robot are sharing their workspace. An Encoder–Decoder based network is proposed to predict the human hand trajectories. A Model Predictive Control (MPC) framework is also proposed, which is able to plan a collision-free trajectory in the shared workspace based on this human motion prediction. The proposed framework is validated in a real environment that ensures collision free collaboration between humans and robots in a shared workspace.

APA, Harvard, Vancouver, ISO, and other styles

40

Al-Faiz, Mohammed Z., and Sarmad H. Ahmed. "Discriminant Analysis for Human Arm Motion Prediction and Classifying." Intelligent Control and Automation 04, no. 01 (2013): 26–31. http://dx.doi.org/10.4236/ica.2013.41004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Zhang, Zhibo, Yanjun Zhu, Rahul Rai, and David Doermann. "PIMNet: Physics-Infused Neural Network for Human Motion Prediction." IEEE Robotics and Automation Letters 7, no. 4 (October 2022): 8949–55. http://dx.doi.org/10.1109/lra.2022.3188892.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Tang, Jin, Jin Zhang, and Jianqin Yin. "Temporal consistency two-stream CNN for human motion prediction." Neurocomputing 468 (January 2022): 245–56. http://dx.doi.org/10.1016/j.neucom.2021.10.011.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

QIAN, Kun, Xudong MA, Xianzhong DAI, and fang FANG. "POMDP Navigation of Service Robots with Human Motion Prediction." ROBOT 32, no. 1 (May 25, 2010): 18–24. http://dx.doi.org/10.3724/sp.j.1218.2010.00018.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Bello, Ghalib A., Timothy J. W. Dawes, Jinming Duan, Carlo Biffi, Antonio de Marvao, Luke S. G. E. Howard, J. Simon R. Gibbs, et al. "Deep-learning cardiac motion analysis for human survival prediction." Nature Machine Intelligence 1, no. 2 (February 2019): 95–104. http://dx.doi.org/10.1038/s42256-019-0019-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Takano, Wataru, Hirotaka Imagawa, and Yoshihiko Nakamura. "Spatio-temporal structure of human motion primitives and its application to motion prediction." Robotics and Autonomous Systems 75 (January 2016): 288–96. http://dx.doi.org/10.1016/j.robot.2015.09.017.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Chao, Yu-Wei, Jimei Yang, Weifeng Chen, and Jia Deng. "Learning to Sit: Synthesizing Human-Chair Interactions via Hierarchical Control." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 7 (May 18, 2021): 5887–95. http://dx.doi.org/10.1609/aaai.v35i7.16736.

Full text

Abstract:

Recent progress on physics-based character animation has shown impressive breakthroughs on human motion synthesis, through imitating motion capture data via deep reinforcement learning. However, results have mostly been demonstrated on imitating a single distinct motion pattern, and do not generalize to interactive tasks that require flexible motion patterns due to varying human-object spatial configurations. To bridge this gap, we focus on one class of interactive tasks---sitting onto a chair. We propose a hierarchical reinforcement learning framework which relies on a collection of subtask controllers trained to imitate simple, reusable mocap motions, and a meta controller trained to execute the subtasks properly to complete the main task. We experimentally demonstrate the strength of our approach over different non-hierarchical and hierarchical baselines. We also show that our approach can be applied to motion prediction given an image input. A supplementary video can be found at https://youtu.be/3CeN0OGz2cA.

APA, Harvard, Vancouver, ISO, and other styles

47

Wang, Juxing, and Linyong Shen. "Semi-Adaptable Human Hand Motion Prediction Based on Neural Networks and Kalman Filter." Journal of Physics: Conference Series 2029, no. 1 (September 1, 2021): 012091. http://dx.doi.org/10.1088/1742-6596/2029/1/012091.

Full text

Abstract:

Abstract This paper focuses on predicting trajectories of the human hand in order to improve the safety for human-robot interactions. In this work, the position and orientation are represented by two curves in the operation space such that the same algorithm can be used for both position and orientation prediction. The motion prediction is achieved in two steps. Firstly, the neural network (NN) model is applied for offline training to model the human hand motion. Secondly, the Kalman filter is added to adjust the weight coefficients of the NN model’s output layer online when a set of new data is measured, such that the NN model is adaptive to new data. An experiment study has been conducted to validate the effectiveness of the proposed algorithm. The result shows that the proposed algorithm achieves a higher prediction accuracy and requires a smaller amount of data to achieve optimal performance compared with the advanced method.

APA, Harvard, Vancouver, ISO, and other styles

48

Moon, Hee-Seung, and Jiwon Seo. "Fast User Adaptation for Human Motion Prediction in Physical Human–Robot Interaction." IEEE Robotics and Automation Letters 7, no. 1 (January 2022): 120–27. http://dx.doi.org/10.1109/lra.2021.3116319.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Diraneyya, Mohsen M., JuHyeong Ryu, Eihab Abdel-Rahman, and Carl T. Haas. "Inertial Motion Capture-Based Whole-Body Inverse Dynamics." Sensors 21, no. 21 (November 5, 2021): 7353. http://dx.doi.org/10.3390/s21217353.

Full text

Abstract:

Inertial Motion Capture (IMC) systems enable in situ studies of human motion free of the severe constraints imposed by Optical Motion Capture systems. Inverse dynamics can use those motions to estimate forces and moments developing within muscles and joints. We developed an inverse dynamic whole-body model that eliminates the usage of force plates (FPs) and uses motion patterns captured by an IMC system to predict the net forces and moments in 14 major joints. We validated the model by comparing its estimates of Ground Reaction Forces (GRFs) to the ground truth obtained from FPs and comparing predictions of the static model’s net joint moments to those predicted by 3D Static Strength Prediction Program (3DSSPP). The relative root-mean-square error (rRMSE) in the predicted GRF was 6% and the intraclass correlation of the peak values was 0.95, where both values were averaged over the subject population. The rRMSE of the differences between our model’s and 3DSSPP predictions of net L5/S1 and right and left shoulder joints moments were 9.5%, 3.3%, and 5.2%, respectively. We also compared the static and dynamic versions of the model and found that failing to account for body motions can underestimate net joint moments by 90% to 560% of the static estimates.

APA, Harvard, Vancouver, ISO, and other styles

50

Callens, Thomas, Tuur van der Have, Sam Van Rossom, Joris De Schutter, and Erwin Aertbelien. "A Framework for Recognition and Prediction of Human Motions in Human-Robot Collaboration Using Probabilistic Motion Models." IEEE Robotics and Automation Letters 5, no. 4 (October 2020): 5151–58. http://dx.doi.org/10.1109/lra.2020.3005892.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Human Motion Prediction'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles