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Implementing gesture recognition on ARM as an alternative to traditional device control
Gajdošík, Richard ; Zbořil, František (oponent) ; Kočí, Radek (vedoucí práce)
This bachelor's thesis focuses on the development and implementation of a gesture recognition system on ARM architecture, utilizing the i.MX 93 board and TensorFlow Lite. The project is grounded in the application of neural networks for the recognition of hand gestures, offering an alternative to traditional device control methods. An integral part of the work involves a comprehensive analysis of existing gesture recognition solutions, identifying their strengths and potential improvements. The thesis elaborates on the design, development, and optimization of a real-time gesture recognition model specifically for ARM chips, emphasizing efficiency and performance. Additionally, the thesis covers the creation of a demonstrative application that visually represents recognized gestures. User testing is conducted to evaluate the practicality and user experience of the gesture recognition system, providing valuable feedback for future enhancements.

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3 Gajdošík, Róbert
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