International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Conventional computer input devices impose physical constraints and accessibility barriers where traditional keyboards and mice provide precise control but require direct surface contact and fine motor coordination. This research presents HandPilot, an integrated wearable computing framework combining multi-sensor fusion with supervised machine learning for intuitive gesture-based human-computer interaction. The system deploys Arduino-based smart glove modules with MPU6050 6-axis inertial measurement unit (3-axis accelerometer and 3-axis gyroscope), resistive flex sensor for finger bend detection, and three tactile push buttons to capture spatially oriented hand movements and discrete click operations. Structured data packets containing accelerometer readings (Ax, Ay, Az), gyroscope measurements (Gx, Gy, Gz), flex sensor values, and button states are transmitted wirelessly via HC-05 Bluetooth module at 9600 baud rate to a receiver Arduino connected to host computer through USB serial interface. A comprehensive labeled dataset mapping ten gesture classes—right, left, up, down, zoom in, zoom out, drag, left-click, right-click, and double-click—to corresponding sensor feature vectors enables training of multiple supervised classification algorithms including K-Nearest Neighbors (KNN), Support Vector Machines (SVM) with RBF kernel, Decision Trees with Gini impurity splitting, and Random Forest ensemble methods. The trained Random Forest model integrates into a Python-based real-time control engine using PySerial for serial communication and data parsing, combined with PyAutoGUI for cross-platform mouse action execution and cursor control. Experimental validation demonstrates superior performance, achieving 94% classification accuracy with the Random Forest classifier, representing 30% improvement over conventional threshold-based gesture recognition approaches, with end-to-end system latency of 80-120ms and 99.7% data transmission reliability during 24-hour continuous operation testing.

Keywords: Gesture Recognition, Wearable Computing, Machine Learning, Human-Computer Interaction, MPU6050, Bluetooth Communication, Assistive Technology

Downloads: | DOI: 10.17148/IJARCCE.2025.1412140