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Abstract
Notably, the research addresses challenges in achieving precision and user adaptability within Virtual Gesture Systems. Employing a systematic methodology, the study utilizes cutting-edge hardware and software to capture and analyze data related to hand motion. Additionally, this research integrates a voice assistant-based chatbot with limited functions, expanding the scope of interaction possibilities. The results offer a comprehensive evaluation of the combined Virtual Gesture System and voice-assisted chatbot. This evaluation includes a comparative analysis of accuracy, efficiency, and user satisfaction against existing systems in the field.
The ensuing discussion interprets the findings, addressing initial research questions and outlining implications for future development. The integration of a voice assistant-based chatbot enhances the versatility of the system, opening avenues for streamlined and user-friendly interactions. Despite inherent limitations, this research significantly contributes to the understanding of Virtual Gesture Systems, presenting a novel approach to human-computer interaction across diverse domains.
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References
. Python Machine Learning by Sebastian Raschka.
. Machine Learning in Data Science Using Python by Dr. R. Nageswara Rao.
. https://en.wikipedia.org/wiki/Machine_learning
. https://www.w3schools.com/python/python_ml_getting_started.asp