Gesture Recognition Using TinyML Model Implementation On Low Cost Microcontroller For Appliance Control
Keywords:
Gesture Recognition, TinyML, ESP32-CAM, MediaPipe, Hand Landmark Detection, Smart Home Automation, Relay-Based Appliance Control, Edge Computing, Low-Cost Embedded System etc.,Abstract
Gesture Control Systems provide a contactless control mechanism of electrical appliances in smart homes which is intuitive. The current paper introduces a low-cost gesture recognition system that is carried out with the ESP32-CAM microcontroller and a simplistic TinyML-based vision system. When the system is launched, all hardware equipment, such as the camera, relay modules, LCD display, and wireless network is initialized to make sure that it works reliably. The ESP32-CAM will constantly take real-time images of hands and send them to an edge-processing unit, which loads a hand landmark detection model based on MediaPipes. Images that have been captured are preprocessed by ensuring that they are resized and normalized to increase resistance to different illumination conditions. Hand landmarks are extracted into whether there are valid gestures or not and the system classifies the gestures that are recognized whilst ignoring those frames that do not have much significance to reduce false activations. Known gestures are also assigned to a set of control commands and sent back to the ESP32-CAM over Wi-Fi. The microcontroller uses relay modules to switch on and off lights and fans among other appliances, and also to display gesture and appliance status on 16×2 LCD. The operational loop makes it possible to have real-time, reliable, and energy-efficient gesture-based control of appliances, which proves that it is possible to deploy TinyML-assisted vision systems on resource-restricted, low-cost hardware platform.
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