A. Mousavi, A. Sheikh Mohammad Zadeh, M. Akbari, and A. Hunter, “A New Ontology-Based Approach for Human Activity Recognition from GPS Data,” J. AI Data Min., vol. 5, no. 2, pp. 197–210, 2017, [Online]. Available: http://jad.shahroodut.ac.ir/article_889.html.
 S. Li and A. B. Chan, “3D human pose estimation from monocular images with deep convolutional neural network,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, Vol. 9004, pp. 332–347, doi: 10.1007/978-3-319-16808-1_23.
 B. Tekin, I. Katircioglu, M. Salzmann, V. Lepetit, and P. Fua, “Structured Prediction of 3D Human Pose with Deep Neural Networks,” in Procedings of the British Machine Vision Conference 2016, 2016, vol. 2016-September, pp. 130.1-130.11, doi: 10.5244/C.30.130.
 J. Martinez, R. Hossain, J. Romero, and J. J. Little, “A simple yet effective baseline for 3d human pose estimation,” IEEE Int. Conf. Comput. Vis., May 2017, Accessed: Jun. 08, 2020. [Online]. Available: http://arxiv.org/abs/1705.03098.
 Y. Kudo, K. Ogaki, Y. Matsui, and Y. Odagiri, “Unsupervised adversarial learning of 3d human pose from 2d joint locations,” arXiv:1803.08244v1, 2018, [Online]. Available: http://arxiv.org/abs/1803.08244.
 C. H. Chen et al., “Unsupervised 3D pose estimation with geometric self-supervision,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., Vol. 2019-June, pp. 5707–5717, 2019, doi: 10.1109/CVPR.2019.00586.
 S. Tripathi, S. Ranade, A. Tyagi, and A. Agrawal, “PoseNet3D: Unsupervised 3D Human Shape and Pose Estimation,” arXiv:2003.03473v1, 2020.
 N. Pourdamghani, H. R. Rabiee, F. Faghri, and M. H. Rohban, “Graph based semi-supervised human pose estimation: When the output space comes to help,” Pattern Recognit. Lett., Vol. 33, No. 12, pp. 1529–1535, 2012, doi: 10.1016/j.patrec.2012.04.012.
 D. Pavllo, Z. Eth, and C. Feichtenhofer, “3D human pose estimation in video with temporal convolutions and semi-supervised training,” CVPR, 2019.
 R. Mitra, N. B. Gundavarapu, A. Sharma, A. Ai, and A. Jain, “Multiview-Consistent Semi-Supervised Learning for 3D Human Pose Estimation,” 2020.
 B. A. Olshausen and D. J. Field, “Sparse coding with an overcomplete basis set: A strategy employed by V1?,” Vision Res., Vol. 37, No. 23, pp. 3311–3325, Dec. 1997, doi: 10.1016/S0042-6989(97)00169-7.
 C. Wang, Y. Wang, Z. Lin, A. L. Yuille, and W. Gao, “Robust estimation of 3D human poses from a single image,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Sep. 2014, pp. 2369–2376, doi: 10.1109/CVPR.2014.303.
 V. Ramakrishna, T. Kanade, and Y. Sheikh, “Reconstructing 3D human pose from 2D image landmarks,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, Vol. 7575 LNCS, No. PART 4, pp. 573–586, doi: 10.1007/978-3-642-33765-9_41.
 X. Zhou, M. Zhu, S. Leonardos, and K. Daniilidis, “Sparse Representation for 3D Shape Estimation: A Convex Relaxation Approach,” IEEE Trans. Pattern Anal. Mach. Intell., Vol. 39, No. 8, pp. 1648–1661, Sep. 2017, doi: 10.1109/TPAMI.2016.2605097.
 E. J. Candès, M. B. Wakin, and S. P. Boyd, “Enhancing sparsity by reweightedℓ1 minimization,” J. Fourier Anal. Appl., Vol. 14, No. 5–6, pp. 877–905, Dec. 2008, doi: 10.1007/s00041-008-9045-x.
 X. Zhou, M. Zhu, S. Leonardos, K. G. Derpanis, and K. Daniilidis, “Sparseness meets deepness: 3D human pose estimation from monocular video,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Dec. 2016, Vol. 2016-Decem, pp. 4966–4975, doi: 10.1109/CVPR.2016.537.
 X. Fan, K. Zheng, Y. Zhou, and S. Wang, “Pose locality constrained representation for 3D human pose reconstruction,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, Vol. 8689 LNCS, No. PART 1, pp. 174–188, doi: 10.1007/978-3-319-10590-1_12.
 M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process., Vol. 54, No. 11, pp. 4311–4322, Nov. 2006, doi: 10.1109/TSP.2006.881199.
 A. Rakotomamonjy, “Applying alternating direction method of multipliers for constrained dictionary learning,” Neurocomputing, Vol. 106, pp. 126–136, Apr. 2013, doi: 10.1016/j.neucom.2012.10.024.
 B. Di Liu, Y. X. Wang, B. Shen, X. Li, Y. J. Zhang, and Y. J. Wang, “Blockwise coordinate descent schemes for efficient and effective dictionary learning,” Neurocomputing, Vol. 178, pp. 25–35, Feb. 2016, doi: 10.1016/j.neucom.2015.06.096.
 W. Li et al., “Maxdenominator Reweighted Sparse Representation for Tumor Classification,” Sci. Rep., Vol. 7, No. 1, pp. 1–13, Apr. 2017, doi: 10.1038/srep46030.
 M. Jiang, Z. Yu, Y. Zhang, Q. Wang, C. Li, and Y. Lei, “Reweighted sparse representation with residual compensation for 3D human pose estimation from a single RGB image,” Neurocomputing, Vol. 358. pp. 332–343, 2019, doi: 10.1016/j.neucom.2019.05.034.
 H. Medvesek, “Most Common Exercise Mistakes: Are You Doing It Wrong?” https://www.runtastic.com/blog/en/bodyweight-exercise-mistakes/ (accessed Dec. 18, 2020).
 J. Redmon and A. Farhadi, “YOLO v.3,” Tech Rep., pp. 1–6, 2018, [Online]. Available: https://pjreddie.com/media/files/papers/YOLOv3.pdf.
 J. Xiao, “ExYOLO: A small object detector based on YOLOv3 Object Detector,” Procedia CIRP, Vol. 188, No. 2019, pp. 18–25, 2021, doi: 10.1016/j.procs.2021.05.048.