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Category

Modelling / Simulation

Document Type

Paper

Abstract

Fencing requires agility, accuracy, and strategy, yet current training technologies lack flexibility, detailed analytics, and accessibility. This research introduces Al Mubarez, an AI-powered fencing training robot integrating motion-sensing fabric, Internet of Things (IoT) technology, and real-time performance analysis to provide personalized training. The system incorporates mechanical, electronic, and sensory components, including an aluminium framework, motorized actuation systems, and capacitive touch sensors, while software development focuses on motion control, reaction time evaluation, and AI-driven data visualization. AI models process tactile sensors and motion-tracking data in real time to improve adaptability. The initial phase established a stable mechanical structure and controlled movement system, with a sumo-style platform ensuring high stability and an aluminium frame withstanding repeated impacts, confirming its durability. Additionally, the differential movement system enabled smooth mobility, setting the foundation for advanced AI integration. Al Mubarez shows significant promise as an interactive and adaptive training tool, addressing key shortcomings of existing solutions. While AI-powered pattern tracking and comprehensive analytics are still in development, initial findings confirm feasibility, with future research focusing on enhancing AI adaptability and performance assessment.

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