Review Paper on Smart Gesture-Based Equipment Control System

In settings where touch-based interfaces are uncomfortable or unsanitary, gesture-based human–machine interaction has become a popular way to operate smart devices. Gesture recognition systems are now very accurate, responsive, and appropriate for real-world applications thanks to developments in computer vision, deep learning, IoT communication, and embedded CPUs. A thorough theoretical examination of gesture detection technologies is presented in this review paper, with particular attention to CNN-powered gesture classification techniques, MediaPipe hand-tracking models, and OpenCV-based image processing workflows. Additionally, it looks at how Internet of Things microcontrollers like ESP32 can be used to enable wireless, realtime control of electrical appliances through relay modules. In order to determine performance trends, system reliability, and practical issues, the study synthesizes findings from several research investigations. The focus is on developing a smooth and clean control environment that improves user convenience, facilitates accessibility for users with physical disabilities, and aids in the creation of next-generation smart homes. This enhanced assessment is appropriate for academic submissions and engineering research since it blends scientific depth with practical relevance.

Gesture Recognition, Media Pipe, Smart Home Automation, OpenCV, Deep Learning, ESP32, Internet of Things, Human–Computer Interaction, Convolutional Neural Network.

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