DESIGN AND IMPLEMENTATION OF AN INFRARED MOTION DETECTION AND GESTURE CLASSIFICATION SYSTEM USING KALMAN FILTERING AND CONVOLUTIONAL NEURAL NETWORKS

This project report presents the design, development, and implementation of an Infrared Motion Detector and an advanced gesture classification system utilizing Kalman filtering and Convolutional Neural Networks (CNNs). The primary objective is to create a cost-effective, efficient, and reliable system capable of detecting motion and accurately classifying dynamic hand gestures for a wide range of applications, including security systems, smart home automation, and human-computer interaction.The motion detection component is based on the principle of Passive Infrared (PIR) sensing, where the device detects infrared radiation naturally emitted by living beings. When a moving object enters the monitored zone, the sensor captures sudden changes in infrared levels, prompting an appropriate system response, such as activating an alarm or switching on a light. Detailed exploration of the PIR sensor's working mechanism, internal structure, signal processing, and sensitivity calibration is provided.For gesture classification, the system integrates Kalman filtering to track the motion trajectory and eliminate noise from sensor inputs, ensuring smoother and more accurate prediction of hand movement paths. These filtered motion signals are then processed by a Convolutional Neural Network, trained to recognize a variety of hand gestures with high precision. The CNN automatically extracts spatial and temporal features from the input data, allowing for robust classification even under varying lighting and environmental conditions.In conclusion, this project not only showcases the practical implementation of infrared-based motion detection but also demonstrates the power of combining predictive filtering and deep learning techniques for sophisticated gesture recognition. The developed system offers a scalable platform for future innovations in smart interfaces, automation, and security technologies, highlighting the growing significance of sensor-driven and AI-enhanced solutions in modern electronics.