<![CDATA[This research develops a virtual mouse control system that uses real-time hand gesture recognition implemented on an ESP32-CAM–based Internet of Things (IoT) platform. By leveraging OpenCV for image processing, the system translates hand gestures into corresponding mouse actions, including cursor movement, clicking, and scrolling. The study evaluates system performance under different lighting conditions and Wi-Fi speeds. Results show that higher Wi-Fi speeds significantly reduce latency, enabling smoother real time gesture recognition and high definition video output, while lower speeds lead to noticeable delays and reduced accuracy. The system successfully enables remote cursor control through camera captured hand gestures, supporting functions such as left click, right click, scrolling, and dragging. In latency tests performed with an internet speed of approximately 60 Mbps, the system achieved an average delay of about 50 milliseconds. Under optimal lighting conditions with minimal background interference, it accurately tracked hand movements and recognized gestures such as pointing, clicking, dragging, and scrolling in real time, achieving an accuracy rate of 95%. Despite its lower resolution compared to conventional webcams, the ESP32-CAM proves to be an effective solution for virtual mouse control, particularly in scenarios where high-resolution imaging is unnecessary. Its IoT capabilities support remote operation, allowing users to control the virtual mouse from a distance as long as both the ESP32-CAM and the computer remain connected. Overall, the findings highlight the ESP32-CAM based IoT platform as a viable alternative for gesture based interaction in real applications, although further enhancements are needed to improve performance in challenging environments.]]>
