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A theoretical framework of extrinsic feedback based-automated evaluation system for martial arts
Author(s) -
Wan Mohd Rizhan Wan Idris,
Ahmad Rafi Mohamed Eshaq,
Azman Bidin,
Azrul Amri Jamal
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.14.11160
Subject(s) - martial arts , motion capture , computer science , offensive , motion (physics) , context (archaeology) , artificial intelligence , gesture , human–computer interaction , movement (music) , computer vision , engineering , visual arts , art , paleontology , philosophy , operations research , biology , aesthetics
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.  

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