<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Liu, Yuxuan</style></author><author><style face="normal" font="default" size="100%">Jiang, Xijun</style></author><author><style face="normal" font="default" size="100%">Xingge Yu</style></author><author><style face="normal" font="default" size="100%">Huaidong Ye</style></author><author><style face="normal" font="default" size="100%">Chao Ma</style></author><author><style face="normal" font="default" size="100%">Wanyi Wang</style></author><author><style face="normal" font="default" size="100%">Hu, Youfan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A wearable system for sign language recognition enabled by a convolutional neural network</style></title><secondary-title><style face="normal" font="default" size="100%">Nano Energy</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Convolutional neural networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Sign language recognition</style></keyword><keyword><style  face="normal" font="default" size="100%">Stretchable strain sensor</style></keyword><keyword><style  face="normal" font="default" size="100%">Wearable device</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.sciencedirect.com/science/article/pii/S2211285523006043</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">116</style></volume><pages><style face="normal" font="default" size="100%">108767</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Sign language recognition is of great significance to connect the hearing/speech impaired and non-sign language communities. Compared to isolated word recognition, sentence recognition is more practical in real-world scenarios, but is also more complicated because continuous, high-quality sign data with distinct features must be collected and isolated signs must be identified with high accuracy. Here, we propose a wearable sign language recognition system enabled by a convolutional neural network (CNN) that integrates stretchable strain sensors and inertial measurement units attached to the body to perceive hand postures and movement trajectories. Forty-eight Chinese sign language words commonly used in daily life were collected and used to train the CNN model, and an isolated sign language word recognition accuracy of 95.85% was achieved. For sentence-level sign language recognition, we proposed a method that combines multiple sliding windows and uses correlation analysis to improve the CNN recognition performance, achieving a correct rate of 84% for 50 sign language sentence samples, showing good extendibility.</style></abstract></record></records></xml>