Improved Competitive Neural Network for Classification of Human Postures Based on Data from RGB-D Sensors

[1] A. R. Abas. “Adaptive competitive learning neural networks”, Egyptian Informatics Journal, vol. 14, no. 3, 2013, 183–194. Search in Google Scholar

[2] M. A. R. Ahad et al. “Action recognition using kinematics posture feature on 3d skeleton joint locations”, Pattern Recognition Letters, vol. 145, 2021, 216–224. Search in Google Scholar

[3] P. Branco, L. Torgo, and R. P. Ribeiro. “A survey of predictive modeling on imbalanced domains”, ACM Computing Surveys (CSUR), vol. 49, no. 2, 2016, 1–50. Search in Google Scholar

[4] G. Budura, C. Botoca, and N. Miclău. “Competitive learning algorithms for data clustering”, Facta universitatis-series: Electronics and Energetics, vol. 19, no. 2, 2006, 261–269. Search in Google Scholar

[5] B. Cao, S. Bi, J. Zheng, and D. Yang. “Human posture recognition using skeleton and depth information”. In: 2018 WRC Symposium on Advanced Robotics and Automation (WRC SARA), vol. 1, 2018, 275–280, doi: 10.1109/WRCSARA.2018.8584233. Search in Google Scholar

[6] J. Chen, P. Jönsson, M. Tamura, Z. Gu, B. Matsushita, and L. Eklundh. “A simple method for reconstructing a high-quality ndvi time-series data set based on the savitzky–golay filter”, Remote Sensing of Environment, vol. 91, no. 3-4, 2004, 332–344. Search in Google Scholar

[7] K.-H. Chuang, M.-J. Chiu, C.-C. Lin, and J.-H. Chen. “Model-free functional mri analysis using kohonen clustering neural network and fuzzy c-means”, IEEE Transactions on Medical Imaging, vol. 18, no. 12, 1999, 1117–1128. Search in Google Scholar

[8] V. Dutta, and T. Zielinska. “An adversarial explainable artificial intelligence (xai) based approach for action forecasting”, Journal of Automation Mobile Robotics and Intelligent Systems, vol. 14, 2020. Search in Google Scholar

[9] V. Dutta, and T. Zielinska. “Prognosing human activity using actions forecast and structured database”, IEEE Access, vol. 8, 2020, 6098–6116. Search in Google Scholar

[10] V. Farrahi, M. Niemelä, M. Kangas, R. Korpelainen, and T. Jämsä. “Calibration and validation of accelerometer-based activity monitors: A systematic review of machine-learning approaches”, Gait and Posture, vol. 68, 2019, 285–299. Search in Google Scholar

[11] M. Firman. “Rgbd datasets: Past, present and future”. In: IEEE Proc., vol. 1, 2016, 661–673. Search in Google Scholar

[12] R. Hou et al. “Multi-channel network: Constructing efficient gcn baselines for skeleton-based action recognition”, Computers and Graphics, vol. 110, 2023, 111–117. Search in Google Scholar

[13] W. Kasprzak, and B. Jankowski. “Light-weight classification of human actions in video with skeleton-based features”, Electronics, vol. 11, no. 14, 2022, 2145. Search in Google Scholar

[14] V. Kellokumpu, M. Pietikäinen, and J. Heikkilä. “Human activity recognition using sequences of postures”. In: IAPR, Conference on Machine Vision Applications, vol. 1, no. 1, 2022, 570–573. Search in Google Scholar

[15] F. Kruber, J. Wurst, and M. Botsch. “An unsupervised random forest clustering technique for automatic traffic scenario categorization”. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), vol. 1, 2018, 2811–2818. Search in Google Scholar

[16] J. Liu et al. “A graph attention spatio-temporal convolutional network for 3d human pose estimation in video”. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), vol. 1, 2021, 3374–3380. Search in Google Scholar

[17] L. E. Ortiz, V. E. Cabrera, and L. M. Goncalves. “Depth data error modeling of the zed 3d vision sensor from stereolabs”, ELCVIA: Electronic Letters on Computer Vision and Image Analysis, vol. 17, no. 1, 2018, 1–15. Search in Google Scholar

[18] E. J. Palomo, E. Domínguez, R. M. Luque, and J. Munoz. “A competitive neural network for intrusion detection systems”. In: International Conference on Modelling, Computation and Optimization in Information Systems and Management Sciences, vol. 1, 2008, 530–537. Search in Google Scholar

[19] G. Rogez, P. Weinzaepfel, and C. Schmid. “Lcrnet++: Multi-person 2d and 3d pose detection in natural images”, IEEE TPAMI, vol. 42, no. 5, 2019, 1146–1161. Search in Google Scholar

[20] L. Romeo, R. Marani, M. Malosio, A. G. Perri, and T. D’Orazio. “Performance analysis of body tracking with the microsoft azure kinect”. In: 2021 29th Mediterranean Conference on Control and Automation (MED), vol. 1, 2021, 572–577, doi: 10.1109/MED51440.2021.9480177. Search in Google Scholar

[21] D. E. Rumelhart, and D. Zipser. “Feature discovery by competitive learning”. In: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1: Foundations, 151–193. 1986. Search in Google Scholar

[22] R. J. Saner, E. P. Washabaugh, and C. Krishnan. “Reliable sagittal plane kinematic gait assessments are feasible using low-cost webcam technology”, Gait and Posture, vol. 56, 2017, 19–23. Search in Google Scholar

[23] A. Shahroudy, J. Liu, T. Ng, and G. Wang. “Ntu rgb+d: A large scale dataset for 3d human activity analysis”. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, Los Alamitos, CA, USA, 2016, 1010–1019, doi: 10.1109/CVPR.2016.115. Search in Google Scholar

[24] C. SL. “Task compatibility of manipulator postures”, International Journal of Robotics Research, vol. 7(5), 1988, 13–21, doi: 10.1177/027836498800700502. Search in Google Scholar

[25] P. Tommasino, and D. Campolo. “An extended passive motion paradigm for human-like posture and movement planning in redundant manipulators”, Frontiers in Neurorobotics, vol. 11, 2017, doi: 10.3389/fnbot.2017.00065. Search in Google Scholar

[26] S. Vafadar, W. Skalli, A. Bonnet-Lebrun, M. Khalifé, M. Renaudin, A. Hamza, and L. Gajny. “A novel dataset and deep learning-based approach for marker-less motion capture during gait”, Gait and Posture, vol. 86, 2021, 70–76. Search in Google Scholar

[27] X. Wang, G. Liu, Y. Feng, W. Li, J. Niu, and Z. Gan. “Measurement method of human lower limb joint range of motion through human-machine interaction based on machine vision”, Frontiers in Neurorobotics, vol. 15, 2021. Search in Google Scholar

[28] L.-F. Yeung, Z. Yang, K. C.-C. Cheng, D. Du, and R. K.-Y. Tong. “Effects of camera viewing angles on tracking kinematic gait patterns using azure kinect, kinect v2 and orbbec astra pro v2”, Gait and Posture, vol. 87, 2021, 19–26, doi: 10.1016/j.gaitpost.2021.04.005. Search in Google Scholar

[29] T. Zielinska, G. R. R. Coba, and W. Ge. “Variable inverted pendulum applied to humanoid motion design”, Robotica, vol. 39, no. 8, 2021, 1368–1389. Search in Google Scholar