Machine-Learning-Assisted Soft Fiber Optic Glove System for Sign Language Recognition
Renjie Zhu, Dongliang Fan, Lin Jiang, Huijuan Feng, Hongqiang Wang, Jian S. Dai
Abstract
Sign language recognition devices are effective approaches to breaking the communication barrier between signers and non-signers and exploring human-machine interactions. Wearable gloves have been developed for gesture recognition and virtual reality applications by employing flexible sensors for motion detection and machine learning for data analysis. However, most existing wearable devices present limited sign language translating capacity due to the sensors' design and distribution. Here, we propose a cost-effective dual-hand soft fiber optic glove system consisting of multimode soft liquid-core fiber optic sensors, gyroscopes, wireless printed circuit boards, and batteries for sign language translation. In combination with different deep learning techniques and recognition strategies, the glove system can recognize static gestures and dynamic gestures of American Sign Language, and deduce the meaning of sentences by the sequence of gestures. The soft glove system exhibits a broad sign language range (10 numbers, 26 alphabets, 18 words, and 5 sentences meaning prediction), and high recognition accuracy (98.6% for static gestures, 95% for dynamic gestures). The results also present the recognizing capacity for high-correlated gestures (e.g., “M” and “N”). Finally, we demonstrate its application for controlling the motion of a virtual character through 7 discrete commands in the VR interface.