Design and Implementation of a Sensor-Based Wearable Glove for Real-Time Sign Language Recognition

Abstract

This paper presents a real-time sign language conversion using a wearable glove system utilizing multi-sensor data acquisition and machine learning-based gesture classification. The system employs flex sensors to capture finger articulation along with an inertial measurement unit to track hand orientation and real-time movement.” The acquired analog signals are processed and then classified into feature vectors, which are then used for classification of predefined gesture patterns using a trained learning model.