Robust Human Movement Prediction by Completion-Generative Adversarial Networks with Huber Loss
Abstract
In recent years, wearable exoskeleton robots have been growingly used for rehabilitation or movement assistive purposes. Despite the growing application of these robots in various domains, such as physical therapy, the movement synchronization between robots and human bodies remains a challenging problem. This paper aims to achieve better synchronization by predicting human movement. Although several works have been presented in this domain, the robustness of these predictions has received less attention. This paper aims to provide a robust prediction using Completion-Generative Adversarial Networks (CGAN) that are learned based on the Huber loss function. Specifically, we reshape the 3D-joint-position-time series (jointxaxesxtime) into multivariate time series ((jointxaxes) xtime) and pass them to a CGAN. We use the Huber loss function to improve the GAN performance and offer higher robustness against noise in real-world applications. The proposed method is evaluated on an actual human gait dataset and compared with several recent works in this domain. Results show that the proposed method is superior to the previous works in prediction error, particularly in terms of achieving a better signal-to-noise ratio.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
GAN synthesizes data via adversarial generation, alleviates data scarcity and enables domain adaptation, and is used for sEMG data augmentation and cross-device adaptation.
“| GAN | Synthesize data via adversarial generation | Alleviate data scarcity, enable domain | sEMG data augmentation, cross-device |”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [269]
Cites
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766 · see citing works
Adversarial Discriminative Domain Adaptation
Tzeng, E., Hoffman, J., Saenko, K., & Darrell, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.316 · Cited by 5,154 · see citing works
NIPS 2016 Tutorial: Generative Adversarial NetworksPreprint
Goodfellow, I. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.00160 · Cited by 1,735 · see citing works
On Human Motion Prediction Using Recurrent Neural Networks
Martínez, J., Black, M. J., & Romero, J. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.497 · Cited by 1,019 · see citing works
A Robust Version of the Probability Ratio Test
Huber, P. J. (1965) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177699803 · Cited by 570 · see citing works
Pairwise Relationship Guided Deep Hashing for Cross-Modal Retrieval
Yang, E., Deng, C. D., Liu, W., Liu, X., Tao, D., & Gao, X. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10719 · Cited by 344 · see citing works
HP-GAN: Probabilistic 3D Human Motion Prediction via GAN
Barsoum, E., Kender, J. R., & Liu, Z. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00191 · Cited by 327 · see citing works
Learning Human Motion Models for Long-Term Predictions
Ghosh, P. S., Song, J., Aksan, E., & Hilliges, O. (2017) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2017.00059 · Cited by 217 · see citing works
Joint Sparse Representation and Robust Feature-Level Fusion for Multi-Cue Visual Tracking
Lan, X., Ma, A. J., Yuen, P. C., & Chellappa, R. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2481325 · Cited by 216 · see citing works
Generating the Future with Adversarial Transformers
Vondrick, C., & Torralba, A. B. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.319 · Cited by 164 · see citing works
Bayesian Support Vector Regression Using a Unified Loss Function
Chu, W., Keerthi, S. S., & Ong, C. J. (2004) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2003.820830 · Cited by 144 · see citing works
A brain-controlled exoskeleton with cascaded event-related desynchronization classifiers
Lee, K., Liu, D., Perroud, L., Chavarriaga, R., & Millán, J. D. R. (2016) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2016.10.005 · Cited by 136 · see citing works
Long-Term Human Motion Prediction by Modeling Motion Context and Enhancing Motion Dynamics
Tang, Y., Ma, L., Liu, W., & Zheng, W. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/130 · Cited by 132 · see citing works
Adversarial Examples for Hamming Space Search
Yang, E., Liu, T., Deng, C. D., & Tao, D. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2882908 · Cited by 121 · see citing works
Qiu, S., Li, Z., He, W., Zhang, L., Yang, C., & Su, C. (2016) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2016.2566676 · Cited by 117 · see citing works
Marker-Less 3D Human Motion Capture with Monocular Image Sequence and Height-Maps
Du, Y., Wong, Y., Liu, Y., Han, F., Gui, Y., Wang, Z., Kankanhalli, M., & Geng, W. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46493-0_2 · Cited by 109 · see citing works
Efficient human motion prediction using temporal convolutional generative adversarial network
Cui, Q., Sun, H., Kong, Y., Zhang, X., & Li, Y. (2020) · Information Sciences · doi:10.1016/j.ins.2020.08.123 · Cited by 93 · see citing works
Gait Phase Recognition for Lower-Limb Exoskeleton with Only Joint Angular Sensors
Liu, D.-X., Wu, X., Du, W., Wang, C., & Xu, T. (2016) · Sensors · doi:10.3390/s16101579 · Cited by 84 · see citing works
GD-GAN: Generative Adversarial Networks for Trajectory Prediction and Group Detection in Crowds
Fernando, T., Denman, S., Sridharan, S., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20887-5_20 · Cited by 66 · see citing works
A Force Reflected Exoskeleton-Type Masterarm for Human–Robot Interaction
Kim, Y. S., Lee, J., Lee, S., et al. (2005) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2004.832836 · Cited by 64 · see citing works
Pathological Gait Classification Using Kinect v2 and Gated Recurrent Neural Networks
Jun, K., Lee, Y., Lee, S., Lee, D.-W., & Kim, M. S. (2020) · IEEE Access · doi:10.1109/access.2020.3013029 · Cited by 64 · see citing works
Human Motion Prediction using Semi-adaptable Neural Networks
Cheng, Y., Zhao, W., Liu, C., et al. (2019) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc.2019.8814980 · Cited by 55 · see citing works
Zheng, Y., Song, Q., Liu, J., Song, Q., & Yue, Q. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04567-1 · Cited by 52 · see citing works
Motion Optimization and Parameter Identification for a Human and Lower Back Exoskeleton Model
Manns, P., Sreenivasa, M., Millard, M., & Mombaur, K. (2017) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2017.2676355 · Cited by 45 · see citing works
Motion optimization and parameter identification for a human and lower-back exoskeleton model
Manns, P., Sreenivasa, M., Millard, M., et al. (2017) · heiDOK (Heidelberg University) · doi:10.11588/heidok.00022785 · Cited by 41 · see citing works
Robust Support Vector Regression in Primal with Asymmetric Huber Loss
Balasundaram, S. R., & Meena, Y. (2018) · Neural Processing Letters · doi:10.1007/s11063-018-9875-8 · Cited by 40 · see citing works
SpeakerGAN: Speaker identification with conditional generative adversarial network
Chen, L., Liu, Y., Xiao, W., Wang, Y., & Xie, H. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.08.040 · Cited by 29 · see citing works
Lin, C.-J., Wu, C.-X., & Chaovalitwongse, W. (2015) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2014.2357178 · Cited by 28 · see citing works
Design on intelligent perception system for lower limb rehabilitation exoskeleton robot
Ma, W., Zhang, X., & Yin, G. (2016) · International Conference on Ubiquitous Robots and Ambient Intelligence · doi:10.1109/urai.2016.7625785 · Cited by 28 · see citing works
SEMG-BASED NEURO-FUZZY CONTROLLER FOR A PARALLEL ANKLE EXOSKELETON WITH PROPRIOCEPTION
Fan, Y., Guo, Z., & Yin, Y. (2011) · International Journal of Robotics and Automation · doi:10.2316/journal.206.2011.4.206-3590 · Cited by 27 · see citing works
Human Motion Sensing and Recognition
Liu, H., Ju, Z., Ji, X., et al. (2017) · Studies in computational intelligence · doi:10.1007/978-3-662-53692-6 · Cited by 24 · see citing works
Agarwal, P., & Deshpande, A. D. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2891431 · Cited by 23 · see citing works
Human Motion Recognition Using E-textile Sensor and Adaptive Neuro-Fuzzy Inference System
Vu, C. C., & Kim, J. (2018) · Fibers and Polymers · doi:10.1007/s12221-018-8019-0 · Cited by 21 · see citing works
A Study on EMG-Based Human Motion Prediction for Power Assist Exoskeletons
Kiguchi, K. (2007) · IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA) · doi:10.1109/cira.2007.382917 · Cited by 19 · see citing works
Movement prediction for a lower limb exoskeleton using a conditional restricted Boltzmann machine
Chong, E., & Park, F. C. (2016) · Robotica · doi:10.1017/s0263574716000795 · Cited by 15 · see citing works
Automated Activity Recognition with Gait Positions Using Machine Learning Algorithms
Ng, Y. L., Jiang, X., Zhang, Y., et al. (2019) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.2952 · Cited by 15 · see citing works
Human Motion Prediction Using Manifold-Aware Wasserstein GAN
Chopin, B., Otberdout, N., Daoudi, M., & Bartolo, A. (2021) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg52635.2021.9667071 · Cited by 14 · see citing works
Recurrent Transformation of Prior Knowledge Based Model for Human Motion Recognition
Xu, C., He, J., Zhang, X., Cai, H., Duan, S., Tseng, P., & Li, C. (2018) · Computational Intelligence and Neuroscience · doi:10.1155/2018/4160652 · Cited by 11 · see citing works
Motion Prediction of Human Wearing Powered Exoskeleton
Jin, X., Guo, J., Li, Z., et al. (2020) · Mathematical Problems in Engineering · doi:10.1155/2020/8899880 · Cited by 9 · see citing works
A human motion prediction algorithm based on HSMM for SIAT's exoskeleton
Wang, M., Wu, X., Liu, D.-X., et al. (2016) · Chinese Control Conference (CCC) · doi:10.1109/chicc.2016.7553959 · Cited by 3 · see citing works
Soft Meta-Cognitive Neural Network for Classification Problems
Kafiyan, M., & Rouhani, M. (2018) · International Conference on Computer and Knowledge Engineering · doi:10.1109/iccke.2018.8566359 · Cited by 2 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Chen, S., Peng, C., Yang, B., Lin, J., Zhou, L., Jiang, Z., Liu, Z., Liu, Y., & Tang, L. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-026-00083-5 · Cited by 9 · see citing works
- Authors
- Azari, M., Rafiei, H., & Akbarzadeh-T, M.-R.
- Affiliations
- Ferdowsi University of Mashhad (IR)
- Venue
- 2022 29th National and 7th International Iranian Conference on Biomedical Engineering (ICBME)
- Publisher
- IEEE
- Published
- 2022-12-21
- Pages
- 198-204
- OpenAlex
- W4323021403
- Cited by
- 4
- References
- 41
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 2
- FWCI
- 1.53
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- National and International Iranian Conference on Biomedical Engineering (ICBME) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Physical Sciences › Engineering › Control and Systems Engineering › Human Motion and Animation
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Keywords
- human movement prediction, Huber loss, wearable exoskeleton, human-robot synchronization, gait prediction, multivariate time series, noise robustness, rehabilitation robotics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
