RECONOCIMIENTO DE TÉCNICAS OFENSIVAS EN ARTES MARCIALES: UN MAPEO SISTEMÁTICO
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Mar 21, 2024
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May 10, 2024
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Motivation: The precise identification of punches and kicks in martial arts sporting competitions is a critical issue, often complex, and at times subject to controversies due to the subjective judgment of referees. Problem: The subjectivity in assessing punches and kicks during martial arts sporting competitions poses a significant challenge regarding impartiality and accuracy in refereeing. Solution Approach: This study analyzes the most recent contributions to punch and kick recognition in martial arts competitions. It reviews classification techniques, commonly used sensors, and the performance achieved in identifying these movements. Results: The analysis provides a general overview of implemented punch and kick classification techniques. This contributes to understanding recent advancements in this field and how they can enhance objectivity and precision in refereeing martial arts competitions. Conclusions: This study underscores the growing interest in machine learning techniques for classifying punches and kicks in martial arts, encompassing a wide range of classifiers, from traditional methods to deep learning models. The combination of inertial sensors and depth cameras emerges as a promising avenue. Future research is expected to thoroughly compare and characterize these approaches, paving the way for implementing artificial intelligence systems in martial arts competitions and potentially revolutionizing the objectivity in assessing movements in this sport.
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Cristobal Franco, J. J., Aguileta Gümez, A., Moo Mena, F., & Reyes Magaña, J. C. (2024). RECONOCIMIENTO DE TÉCNICAS OFENSIVAS EN ARTES MARCIALES: UN MAPEO SISTEMÁTICO. Revista Ingenierías Universidad De Medellín, 23(44). https://doi.org/10.22395/rium.v23n44a3
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References
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[3] World Taekwondo Federation, Competition Rules and Interpretation, 2022.
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[12] I. Muhammad and Z. Yan, 'Supervised Machine Learning Approaches: A Survey', ICTACT Journal on Soft Computing, vol. 5, no. 3, pp. 946-952, Apr. 2015, https://doi.org/10.21917/ijsc.2015.0133
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[25] A. Tejero-de-Pablos, Y. Nakashima, T. Sato and N. Yokoya, 'Human action recognition-based video summarization for RGB-D personal sports video', 2016 IEEE International Conference on Multimedia and Expo (ICME), 2016, pp. 1-6, https://doi.org/10.1109/ICME.2016.7552938
[26] M. Tits, S. Laraba, E. Caulier, J. Tilmanne, and T. Dutoit, 'UMONS-TAICHI: A multimodal motion capture dataset of expertise in Taijiquan gestures', Data in brief, vol. 19, pp. 1214-1221, Aug. 2018, https://doi.org/10.1016/j.dib.2018.05.088
[27] T. Hachaj and M. R. Ogiela, 'Classification of karate kicks with hidden markov models classifier and angle-based features', 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2018, pp. 1-5, https://doi.org/10.1109/CISP-BMEI.2018.8633251
[28] J. C. Goma, M. S. Bustos, J. A. Sebastian and J. J. E. Macrohon, 'Detection of Taekwondo Kicks Using RGB-D Sensors', Proceedings of the 2019 3rd International Conference on Software and e-Business, 2019, pp. 129-133, https://doi.org/10.1145/3374549.3374576
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[30] Y. Torigoe, Y. Nakamura, M. Fujimoto, Y. Arakawa and K. Yasumoto, 'Strike activity detection and recognition using inertial measurement unit towards kendo skill improvement support system', Sensors and Materials, vol. 32, no. 2, pp. 651-673, Feb. 2020, http://dx.doi.org/10.18494/SAM.2020.2615
[31] B. Emad, O. Atef, Y. Shams, A. El-Kerdany, N. Shorim, A. Nabil and A. Atia, 'iKarate: Karate kata guidance system', Procedia Computer Science, vol. 175, pp. 149-156, 2020, https://doi.org/10.1016/j.procs.2020.07.024
[32] B. Emad, O. Atef, Y. Shams, A. El-Kerdany, N. Shorim, A. Nabil and A. Atia, 'iKarate: Improving karate kata', Procedia Computer Science, vol. 170, pp. 466-473, 2020, https://doi.org/10.1016/j.procs.2020.03.090
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[34] R. Amerineni, L. Gupta, N. Steadman, K. Annauth, C. Burr, S. Wilson, P. Barnaghi and R. Vaidyanathan, 'Fusion models for generalized classification of multi-axial human movement: Validation in sport performance'. Sensors, vol. 21, no. 24, p. 8409, Dec. 2021, https://doi.org/10.3390/s21248409
[35] D. Omcirk, T. Vetrovsky, J. Padecky, S. Vanbelle, J. Malecek and J. J. Tufano, 'Punch trackers: correct recognition depends on punch type and training experience'. Sensors, vol. 21, no. 9, p. 2968, Apr. 2021, https://doi.org/10.3390%2Fs21092968
[36] J. Echeverria, and O. C. Santos, 'KUMITRON: Artificial intelligence system to monitor karate fights that synchronize aerial images with physiological and inertial signals', 26th International Conference on Intelligent User Interfaces-Companion, 2021, pp. 37-39, https://doi.org/10.1145/3397482.3450730
[37] S. K. Yadav, A. Deshmukh, R. V. Gonela, S. B. Kera, K. Tiwari, H. M. Pandey and S. A. Akbar, 'MS-KARD: A Benchmark for Multimodal Karate Action Recognition', 2022 International Joint Conference on Neural Networks (IJCNN), 2022, pp. 1-8, https://doi.org/10.1109/IJCNN55064.2022.9892646
[38] F. Qureshi, and S. Krishnan, 'Design and Analysis of Electronic Head Protector for Taekwondo Sports'. Sensors, vol. 22, no. 4, p. 1415, Feb. 2022, https://doi.org/10.3390/s22041415
[39] I. G. P. Jaya, R. A. Dharmmesta and A. Rizal, 'Application Foot Kick Classification in Taekwondo with Inertia Sensor and Machine Learning', 2022 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob), 2022, pp. 1-6, https://doi.org/10.1109/APWiMob56856.2022.10014067
[40] X. Su, H. Tong and P. Ji, 'Activity recognition with smartphone sensors', Tsinghua Science and Technology, vol. 19 no. 3, pp. 235-249 Jun. 2014. https://doi.org/10.1109/TST.2014.6838194