References
- Ahmed, A. A., & Nyarko, B. (2024). Smart-Watcher: An AI-Powered IoT Monitoring System for Small-Medium Scale Premises. 2024 International Conference on Computing, Networking and Communications (ICNC), 139–143.
- Askari Hosseini, S., & Wolf, P. (2023). Performance indicators in speed climbing: Insights from the literature supplemented by a video analysis and expert interviews. Frontiers in Sports and Active Living, 5.
https://doi.org/10.3389/fspor.2023.1304403 - Bock, S., & Weiß, M. (2019). A Proof of Local Convergence for the Adam Optimizer. 2019 International Joint Conference on Neural Networks (IJCNN), 1–8.
- Diez-Fernández, P., Ruibal-Lista, B., Rico-Díaz, J., Rodríguez-Fernández, J. E., & López-García, S. (2023). Performance Factors in Sport Climbing: A Systematic Review. Sustainability, 15, 16687.
- Ding, X., & Mariano, V. Y. (2023). Research on expression recognition algorithm based on improved convolutional neural network. International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 12645, 787–792. SPIE.
- Dong, M., Fang, Z., Li, Y., Bi, S., & Chen, J. (2021). AR3D: Attention Residual 3D Network for Human Action Recognition. Sensors, 21, 1656.
- Du, X., Li, Y., Cui, Y., Qian, R., Li, J., & Bello, I. (2021, September 3). Revisiting 3D ResNets for Video Recognition. arXiv.
- Foo, L. G., Gong, J., Fan, Z., & Liu, J. (2023). System-Status-Aware Adaptive Network for Online Streaming Video Understanding. 10514–10523.
- Ge, Z., Cao, G., Li, X., & Fu, P. (2020). Hyperspectral Image Classification Method Based on 2D 3D CNN and Multibranch Feature Fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5776–5788.
- Ghosh, K., Bellinger, C., Corizzo, R., Branco, P., Krawczyk, B., & Japkowicz, N. (2024). The class imbalance problem in deep learning. Machine Learning, 113, 4845–4901.
- Hara, K., Kataoka, H., & Satoh, Y. (2018). Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet? 6546–6555.
- Ho, Y., & Wookey, S. (2020). The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling. IEEE Access, 8, 4806–4813.
- International Olympic Committee. (2021, August 6). Women’s Combined Finals—Climbing | Tokyo 2020 Replays. Retrieved March 3, 2024, from International Olympic Committee website:
https://olympics.com/en/video/women-s-combined-finals-climbing-tokyo-2020-replays - Ji, S., Xu, W., Yang, M., & Yu, K. (2013). 3D Convolutional Neural Networks for Human Action Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 221–231.
- Johnson, J. M., & Khoshgoftaar, T. M. (2019). Survey on deep learning with class imbalance. Journal of Big Data, 6, 27.
- Joshi, P., Escriva, D. M., & Godoy, V. (2016). OpenCV By Example. Packt Publishing Ltd.
- Kim, D., Yu, X., & Xiong, S. (2024, August 5). A Practical and Robust Skeleton-Based Artificial Intelligence Algorithm for Multi-Person Fall Detection on Construction Sites Considering Occlusions [SSRN Scholarly Paper]. Rochester, NY: Social Science Research Network.
- Kong, Y., Satarboroujeni, B., & Fu, Y. (2016). Learning hierarchical 3D kernel descriptors for RGB-D action recognition. Computer Vision and Image Understanding, 144, 14–23.
- Krawczyk, B. (2016). Learning from imbalanced data: Open challenges and future directions. Progress in Artificial Intelligence, 5, 221–232.
- Lau, E. (2021). Identifying physiological demands of Speed Climbing within a sample of recreational climbers.
https://doi.org/10.13140/RG.2.2.18266.06089 - Legreneur, P., Rogowski, I., & Durif, T. (2019). Kinematic analysis of the speed climbing event at the 2018 Youth Olympic Games. Computer Methods in Biomechanics and Biomedical Engineering, 22, S264–S266.
- Liqin, L., Blancaflor, E., & Abisado, M. (2023). Research on Pedestrian Recognition Based on Deep Learning. 2023 5th International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), 18–22.
- Mandour, H. Y. (2024). Artificial Intelligence in Measurement and Evaluation for Athletes. International Sports Science Alexandria Journal, 6, 7–9.
- Masko, D., & Hensman, P. (2015). The Impact of Imbalanced Training Data for Convolutional Neural Networks. Retrieved from
https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166451 - Pandurevic, D., Draga, P., Sutor, A., & Hochradel, K. (2022). Analysis of Competition and Training Videos of Speed Climbing Athletes Using Feature and Human Body Keypoint Detection Algorithms. Sensors, 22, 2251.
- Pandurevic, D., Sutor, A., & Hochradel, K. (2023). Towards statistical analysis of predictive parameters in competitive speed climbing. Sports Engineering, 26, 1–12.
- Pieprzycki, A., Mazur, T., Krawczyk, M., Krol, D., Witek, M., & Rokowski, R. (2023). Computer-Aided Methods for Analysing Run of Speed Climbers.
https://doi.org/10.20944/preprints202302.0166.v1 - Qiu, Z., Yao, T., & Mei, T. (2017). Learning Spatio-Temporal Representation With Pseudo-3D Residual Networks. 5533–5541.
- Reveret, L., Chapelle, S., Quaine, F., & Legreneur, P. (2020). 3D Visualization of Body Motion in Speed Climbing. Frontiers in Psychology, 11.
https://doi.org/10.3389/fpsyg.2020.02188 - Richter, J., Beltrán, R., Köstermeyer, G., & Heinkel, U. (2023). Climbing with Virtual Mentor by Means of Video-Based Motion Analysis: Proceedings of the 3rd International Conference on Image Processing and Vision Engineering, 126–133. Prague, Czech Republic: SCITEPRESS - Science and Technology Publications.
- Saul, D., Steinmetz, G., Lehmann, W., & Schilling, A. F. (2019). Determinants for success in climbing: A systematic review. Journal of Exercise Science & Fitness, 17, 91–100.
- Tao, T., & Long, J. (2023). RETRACTED ARTICLE: Simulation of video image detection in leisure sports tourism industry based on convolutional neural network. Soft Computing.
https://doi.org/10.1007/s00500-023-08426-z - Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2015). Learning Spatiotemporal Features With 3D Convolutional Networks. 4489–4497.
- Vujovic, Z. (2021). Classification Model Evaluation Metrics. International Journal of Advanced Computer Science and Applications, Volume 12, 599–606.