Real-Time Hand Gesture Recognition for LED Control using CNN and Arduino
Objective
This project presents a real-time hand gesture recognition system for controlling LEDs using a Convolutional Neural Network (CNN) and Arduino. The system captures hand gestures through a camera and processes them using a trained CNN model for gesture classification. Recognized gestures are sent to an Arduino microcontroller to control LED operations in real time. The proposed approach offers an intuitive, touchless, and efficient human–machine interaction system.
Abstract
Indoor environmental conditions play an important role in maintaining employee health, comfort, and productivity in modern workspaces. This project presents an IoT-Based Assessment in Workspaces: Indoor Air Quality vs Thermal Comfort using environmental monitoring and automated control techniques. The proposed system uses an Arduino microcontroller integrated with DHT11 and MQ135 sensors to monitor temperature, humidity, and air quality conditions inside workplace environments. A NodeMCU module is used for IoT-based cloud uploading and continuous environmental monitoring. An LCD display shows live environmental parameters and system conditions. The system uses a relay-controlled Peltier module for thermal comfort management, where the module operates in cooling mode when temperature increases and switches to heating mode when temperature decreases. An MQ135 gas sensor is used for detecting harmful gases and poor air quality conditions. If abnormal gas concentration is detected, a CPU fan is automatically activated to improve air circulation and maintain suitable indoor air quality. The proposed system improves workplace environmental monitoring, supports thermal comfort management, enhances indoor air quality control, and promotes healthier and smarter workspace conditions using IoT technology and automation techniques.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Paper Publishing
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