Muhammad Shahril, Ahmad Suffian and Isawasan, Pradeep and Song Quan, Ong and Ahmad Salleh, Khairulliza
(2024)
Silat-AI: Transforming silat gayong training with AI-enhanced pose detection / Ahmad Suffian Muhammad Shahril ... [et al.].
In: UNSPECIFIED.
Abstract
The Silat-AI innovatively applies artificial intelligence (AI) to enhance the training of Silat Gayong, a traditional Malaysian martial art. This web-based system uses a camera to capture and analyze practitioners' movements in real time. By employing machine learning models, specifically Random Forest, the system achieves high accuracy in recognizing and classifying martial arts techniques. This not only modernizes the learning experience but also makes Silat training more accessible and appealing to today's learners, blending traditional practices with modern technology.
Metadata
Item Type: | Conference or Workshop Item (Paper) |
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Creators: | Creators Email / ID Num. Muhammad Shahril, Ahmad Suffian UNSPECIFIED Isawasan, Pradeep UNSPECIFIED Song Quan, Ong UNSPECIFIED Ahmad Salleh, Khairulliza UNSPECIFIED |
Contributors: | Contribution Name Email / ID Num. Chief Editor Abdul Rahman, Zarinatun Ilyani UNSPECIFIED Editor Mohd Nasir, Nur Fatima Wahida UNSPECIFIED Editor Kamarudin, Syaza UNSPECIFIED Designer Ramlie, Mohd Khairulnizam UNSPECIFIED |
Subjects: | T Technology > T Technology (General) > Information technology. Information systems |
Divisions: | Universiti Teknologi MARA, Perak > Seri Iskandar Campus > Faculty of Architecture, Planning and Surveying |
Journal or Publication Title: | The 13th International Innovation, Invention & Design Competition 2024 |
Page Range: | pp. 361-364 |
Keywords: | artificial intelligence, machine learning, pose landmark detection, classification, silat gayong |
Date: | 2024 |
URI: | https://ir.uitm.edu.my/id/eprint/105040 |