Sign Language Recognition based on Data Glove with Bending Sensor
Yunhao Zhang, Lei Jing · 2024
This paper presents the development of a data glove incorporating bend sensors and an IMU to capture gestures representing 26 American Sign Language (ASL) letters, including both static and dynamic gestures. The gestures are then classified using a custom LSTM model, achieving an accuracy of 98.63%.It provides good prospects for enhancing communication accessibility for the deaf community through accurate sign language interpretation.