Using Multiple Sensors for Mobile Sign Language Recognition

Abstract

We build upon a constrained, lab-based Sign Language recognition system with the goal of making it a mobile assistive technology. We examine using multiple sensors for disambiguation of noisy data to improve recognition accuracy. Our experiment compares the results of training a small gesture vocabulary using noisy vision data, accelerometer data and both data sets combined.