A Component-Based Vocabulary-Extensible Sign Language Gesture Recognition FrameworkReportar como inadecuado

A Component-Based Vocabulary-Extensible Sign Language Gesture Recognition Framework - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China


Author to whom correspondence should be addressed.

Academic Editor: Vittorio M. N. Passaro

Abstract Sign language recognition SLR can provide a helpful tool for the communication between the deaf and the external world. This paper proposed a component-based vocabulary extensible SLR framework using data from surface electromyographic sEMG sensors, accelerometers ACC, and gyroscopes GYRO. In this framework, a sign word was considered to be a combination of five common sign components, including hand shape, axis, orientation, rotation, and trajectory, and sign classification was implemented based on the recognition of five components. Especially, the proposed SLR framework consisted of two major parts. The first part was to obtain the component-based form of sign gestures and establish the code table of target sign gesture set using data from a reference subject. In the second part, which was designed for new users, component classifiers were trained using a training set suggested by the reference subject and the classification of unknown gestures was performed with a code matching method. Five subjects participated in this study and recognition experiments under different size of training sets were implemented on a target gesture set consisting of 110 frequently-used Chinese Sign Language CSL sign words. The experimental results demonstrated that the proposed framework can realize large-scale gesture set recognition with a small-scale training set. With the smallest training sets containing about one-third gestures of the target gesture set suggested by two reference subjects, 82.6 ± 13.2% and 79.7 ± 13.4% average recognition accuracy were obtained for 110 words respectively, and the average recognition accuracy climbed up to 88 ± 13.7% and 86.3 ± 13.7% when the training set included 50~60 gestures about half of the target gesture set. The proposed framework can significantly reduce the user’s training burden in large-scale gesture recognition, which will facilitate the implementation of a practical SLR system. View Full-Text

Keywords: sign language recognition; surface electromyography; accelerometer; gyroscope sign language recognition; surface electromyography; accelerometer; gyroscope

Autor: Shengjing Wei, Xiang Chen * , Xidong Yang, Shuai Cao and Xu Zhang

Fuente: http://mdpi.com/


Documentos relacionados