Silat-AI: Transforming silat gayong training with AI-enhanced pose detection / Ahmad Suffian Muhammad Shahril ... [et al.]

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)
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
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105040

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