Chinese Sign Language Alphabet Recognition Based on Random Forest Algorithm

Autor:	Guanglin Li, Shurui Sun, Yuan Wang, Hanjie Deng, Xin Wang, Yuan Simin, Hui Wang, Pingao Huang
Rok vydání:	2020
Předmět:	Support vector machine Artificial neural network Computer science Speech recognition Feature extraction SIGNAL (programming language) Process (computing) language Sign language Chinese Sign Language language.human_language Random forest
Zdroj:	MetroInd4.0&IoT Web of Science
Popis:	Sign language is the language deaf-mute people use to communicate with each other. While people with normal hearing generally can not understand it. Sign language recognition allows hard of hearing people to communicate with general society. In this study, we utilized surface Electromyography (sEMG) to recognize Chinese Sign Language alphabet which is an important part of Chinese Sign Language and recognizing them accurately is critical. For this purpose we attached 8 sEMG sensors on the right forearm of the subjects and collected sEMG signal when they were performing all the 30 alphabet letters. Random forest algorithm was used to classify the data after filtering and feature extraction process. Experimental results showed that random forest algorithm achieved an average recognition rate of 95.48% which was higher than Artificial Neural Networks (ANN) and Support Vector Machine (SVM) and had a more stable performance.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::06513af695fc3cba0355309440b4675e https://doi.org/10.1109/metroind4.0iot48571.2020.9138285 Zobrazit plný text záznamu