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Holden, D., Kanoun, O., Perepichka, M., et al. (2020). Learned motion matching. ACM Transactions on Graphics, 39(4), Article No. 53.Search in Google Scholar
Nema, S., Kowalczyk, P., Loram, I. (2017). Wavelet-frequency analysis for the detection of discontinuities in switched system models of human balance. Human Movement Science, 51(Complete), 27-40.Search in Google Scholar
Bonnell, Tyler, R., et al. (2017). Direction matching for sparse movement data sets: determining interaction rules in social groups. Behavioral Ecology.Search in Google Scholar
Huang, Y. H., Kaufmann, M., Aksan, E., et al. (2018). Deep inertial poser: learning to reconstruct human pose from sparse inertial measurements in real time. ACM Transactions on Graphics, 37(6), Article No. 185.Search in Google Scholar
Bulbul, M. F., Jiang, Y. S., Ma, J. W. (2015). DMMs-based multiple features fusion for human action recognition. International Journal of Multimedia Data Engineering and Management, 6(4), 23-39.Search in Google Scholar
Haghighat, A. M., Abdel-Mottaleb, M., Alhalabi, W. (2016). Discriminant correlation analysis: real-time feature level fusion for multimodal biometric recognition. IEEE Transactions on Information Forensics and Security, 11(9), 1984-1996.Search in Google Scholar
von Marcard, T., Rosenhahn, B., Black, M. J., et al. (2017). Sparse inertial poser: Automatic 3D human pose estimation from sparse IMUs. Computer Graphics Forum, 36(2), 349-360.Search in Google Scholar
Li, S. J., Zhou, Y., Zhu, H. S., et al. (2019). Bidirectional recurrent autoencoder for 3D skeleton motion data refinement. Computers & Graphics, 81, 92-103.Search in Google Scholar
Holden, D., Saito, J., Komura, T. (2016). A deep learning framework for character motion synthesis and editing. ACM Transactions on Graphics, 35(4), Article No. 138.Search in Google Scholar
Elmadany, N. E. D., He, Y. F., Guan, L. (2019). Multimodal learning for human action recognition via bimodal/multimodal hybrid centroid canonical correlation analysis. IEEE Transactions on Multimedia, 21(5), 1317-1331.Search in Google Scholar
Yang, T. J., Hou, Z. J., Liang, J. Z., et al. (2020). Depth sequential information entropy maps and multi-label subspace learning for human action recognition. IEEE Access, 8, 135118-135130.Search in Google Scholar
Zhao, X., & Zhou, J. (2022). Fast recognition algorithm for human motion posture using multimodal bioinformation fusion. Mathematical Problems in Engineering.Search in Google Scholar
Ehatisham-UL-Haq, M., Javed, A., Azam, M. A., et al. (2019). Robust human activity recognition using multimodal feature-level fusion. IEEE Access, 7, 60736-60751.Search in Google Scholar
Ahmad, Z., Khan, N. (2021). CNN-based multistage gated average fusion for human action recognition using depth and inertial sensors. IEEE Sensors Journal, 21(3), 3623-3634.Search in Google Scholar
Tu, Z. G., Xie, W., Qin, Q. Q., et al. (2018). Multi-stream CNN: learning representations based on human-related regions for action recognition. Pattern Recognition, 79, 32-43.Search in Google Scholar
Majumder, S., Kehtarnava, N. (2021). Vision and inertial sensing fusion for human action recognition: a review. IEEE Sensors Journal, 21(3), 2454-2467.Search in Google Scholar
Fain, B. G., & Dobrovolny, H. M. (2022). Gpu acceleration and data fitting: agent-based models of viral infections can now be parameterized in hours. Journal of computational science, 61.Search in Google Scholar