A theoretical framework of extrinsic feedback based-automated evaluation system for martial arts

  • Authors

    • Wan Mohd Rizhan Wan Idris
    • Ahmad Rafi
    • Azman Bidin
    • Azrul Amri Jamal
    2018-04-06
    https://doi.org/10.14419/ijet.v7i2.14.11160
  • Martial Arts, Motion Capture, Gesture Description Language, Reverse-Gesture Description Language.

  • Martial arts (MAs) are considered as a preserved heritage primarily due to the fact that it promotes certain level of identities of a culture. MA refers to the art of combat and self-defense which normally combines offensive and defensive techniques. Technology advancements have made motion capture (MoCap) to be widely used in MA to capture and evaluate human performance. Nevertheless, researches on extrinsic feedbacks (EFs) of MA through the developed evaluation system are scarce. Furthermore, there is no complete framework of evaluation system suggested for MA. This paper presents the theoretical framework of EF-based automated evaluation system in the context of traditional local MA. The framework contains three modules including MoCap, recognition and evaluation. The MoCap module tracks human body accurately in order to generate skeleton, tune focused target, and record human movements. Recognition module develops a script of motion for templates and classification purposes using Reverse-Gesture Description Language (R-GDL) and GDL respectively. Evaluation module produces the extrinsic feedback in terms of pattern and score for the performed movements. This theoretical framework will be used in the development of the digital tool to measure the accuracy and effectiveness of motions performed by one of the traditional local MAs.

     

     

  • References

    1. [1] L. Kovavisaruch, J. Wisanmongkol, T. Sanpachuda, A. Chaiwongyen, S. Wisadsud, T. Wongsatho, B. Tangkamcharoen, B. Nagarachinda & C. Khiawchaum, Conserving and Promoting Thai Sword Dancing Traditions with Motion Capture and the Nintendo Wii, IEEE Conference Publications, Proceeding of PICMET’ 11, Technology Management in the Energy Smart World (PICMET), 2011, pp: 1-5

      [2] Mark McPherson, William Pickett, Characteristics of martial art injuries in a defined Canadian population: a descriptive epidemiological study, McPherson and Pickett BMC Public Health, 10:795 (2010) 1471-2458.

      [3] Ghazirah Mustapha, Jamaluddin Mahmud, Muzammer Zakaria, Wan Ruzaini Wan Sulaiman, Biomechanics research on martial arts – the importance of defensive study, Archives of Budo: Science of Martial Arts, 11 (2015), 187-195.

      [4] Abdul Majid Ismail, Penerapan jati diri melalui ilmu bela diri: Pendekatan Seni Silat Cekak Malaysia, Prosiding Persidangan Kebangsaan Kerja Sosial 2002: Belia & NGO dalam kerja sosial: Isu peranan dan strategi, 13-15 Ogos 2002, Hotel Legend, Kuala Lumpur, Malaysia.

      [5] Thomas Draxler, Herwig Ostermann, Wilfried Honekamp, Relationship between Asian martial arts and health-related quality of life in Germany, Journal of Public Health (2011) 19:57–64.

      [6] Fernandes, FM., Wichi, RB., Silva, VF., Ladeira, APX., Ervilha, UF., Biomechanical methods applied in martial arts studies, J. Morphol. Sci., 2011, vol. 28, no. 3, p. 141-144.

      [7] Massimo Mariconda, Andrea Cozzolino, Francesco Di Pietto, Manuel Ribas, Vittorio Bellotti, Alessandra Soldati, Radiographic findings of femoroacetabular impingement in capoeira players, Knee Surg Sports Traumatol Arthrosc (2014) 22:874–881.

      [8] Joseph F. Baker, Brian M. Devitt, Ray Moran, Anterior cruciate ligament rupture secondary to a ‘heel hook’: A dangerous martial arts technique, Knee Surg Sports Traumatol Arthrosc (2010) 18:115–116.

      [9] K. Kolykhalova, A. Camurri, G. Volpe, M. Sanguineti, E. Puppo, R. Niewiadomski, A multimodal dataset for the analysis of movement qualities in karate martial art, 2015 7th International Conference on Intelligent Technologies for Interactive Entertainment (INTETAIN), pp: 74-78.

      [10] Alberto Menache (2011), Understanding Motion Capture For Computer Animation: Second Edition, Morgan Kaufmann: USA.

      [11] Benitezsantiago, Angela Stefanie, "Using Video Feedback to Improve Martial-Arts Performance" (2011). Graduate Theses and Dissertations. http://scholarcommons.usf.edu/etd/3006

      [12] Guohua Zheng, Moyi Li, Xiulu Lan, Xinghui Yan, Qiu Lin, Lidian Chen, Jing Tao, Xin Zheng, Junzhe Li, Bai Chen, Qianying Fang, The effect of Baduanjin exercise for physical and psychological wellbeing of college students: study protocol for a randomized controlled trial, Trials, 14: 1, 2013.

      [13] Yu, H., Evaluation model of fuzzy data and neural network in Martial art competition, Journal of Theoretical and Applied Information Technology, 48: 3, 2013, 1458-1465.

      [14] Chang-Hoon Choi, Hae-Jong Joo, Motion recognition technology based remote Taekwondo Poomsae evaluation system, Multimedia Tools and Applications, 1–14, (2015).

      [15] Ghazirah Mustapha, Muzammer Zakaria, Wan Ruzaini Wan Sulaiman, Jamaluddin Mahmud, The mechanical aspects of martial arts: Total time of execution and kinematics of Kaedah A, Proceeding of the International Colloqium on Sports, Science, Exercise, Engineering and Technology 2014, 3-12.

      [16] Portal Rasmi Persatuan Silat Kebangsaan Malaysia (PESAKA), http://www.pesakamalaysia.org.my/, Accessed on June 16, 2016.

      [17] Cairul Anwar Bin Mohamed (June 20, 2017), Personal Interview, Faculty of Economy & Management Science, UniSZA, Gong Badak Campus.

      [18] Connsynn Chye, Mizuki Sakamoto and Tatsuo Nakajima, An exergame for encouraging martial arts, Human-Computer Interaction. Applications and Services, 2014.

      [19] Tomasz Hachaj, Marek R. Ogiela, Katarzyna Koptyra, Effectiveness comparison of Kinect and Kinect 2 for recognition of Oyama karate techniques, NBiS 2015 - The 18-th International Conference on Network-Based Information Systems (NBiS 2015), September 2-4, Taipei, Taiwan, 332-337.

      [20] Tomasz Hachaj and Marek R. Ogiela, Computer Karate Trainer in Tasks of Personal and Homeland Security Defense, Security Engineering and Intelligence Informatics, 2013.

      [21] Tomasz Hachaj & Marek R. Ogiela, Rule-based approach to recognizing human body poses and gestures in real time, Multimedia Systems (2014) 20:81–99.

      [22] Tomasz Hachaj, Marek R. Ogiela, Marcin Piekarczyk, Dependence of Kinect sensors number and position on gestures recognition with Gesture Description Language semantic classifier, Preprints of the Federated Conference on Computer Science and Information Systems (2013), pp. 575–579

      [23] Tomasz Hachaj, Marek R. Ogiela, Full body movements recognition – unsupervised learning approach with heuristic R-GDL method, Digital Signal Processing, Volume 46, November 2015, Pages 239–252.

      [24] Tomasz Hachaj, Marek R. Ogiela, Katarzyna Koptyra, Application of Hidden Markov Models and Gesture Description Language classifiers to Oyama karate techniques recognition, Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), 2015 9th International Conference on, 8-10 July 2015, Blumenau, pp. 160 – 165.

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  • How to Cite

    Mohd Rizhan Wan Idris, W., Rafi, A., Bidin, A., & Amri Jamal, A. (2018). A theoretical framework of extrinsic feedback based-automated evaluation system for martial arts. International Journal of Engineering & Technology, 7(2.14), 74-79. https://doi.org/10.14419/ijet.v7i2.14.11160