International Journal of Advanced Research in Computer and Communication Engineering

Abstract: This paper presents a comprehensive research paper on gesture-guided unmanned aerial vehicle ( UAV ) control systems utilizing the ESP32 microcontroller and MPU6050 inertial measurement unit (IMU). The integration of wearable gesture recognition with wireless drone control offers intuitive, hands-free operation for aerial vehicles. Through detailed analysis of hardware architecture, sensor fusion algorithms, machine learning approaches, and real-time signal processing, this work demonstrates a cost-effective alternative to traditional remote control interfaces. Key findings show that gesture recognition systems combining accelerometer and gyroscope data achieve recognition accuracies exceeding 98 percentage with processing latencies under 50 milliseconds. The hybrid approach utilizing 1D convolutional neural networks and LSTM architectures enables both static and dynamic gesture classification suitable for real-time drone navigation. This research establishes foundational principles for accessible, intuitive aerial vehicle control systems with applications extending to entertainment, surveillance, search-and-rescue, and emergency response operations.

Keywords: Gesture Recognition, Smart Glove, ESP32, MPU6050, UAV Control, Human-Machine Interaction, Wearable Sensors

Downloads: | DOI: 10.17148/IJARCCE.2025.141150