A Component-Based Vocabulary-Extensible Sign Language Gesture Recognition Framework.
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.
Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China. wt901218@mail.ustc.edu.cn.
Shengjing
S
Wei
Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China. xch@ustc.edu.cn.
Xiang
X
Chen
Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China. yangxid@mail.ustc.edu.cn.
Xidong
X
Yang
Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China. caoshuai@mail.ustc.edu.cn.
Shuai
S
Cao
Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China. xuzhang90@ustc.edu.cn.
Xu
X
Zhang
D000328:Adult; D015374:Biosensing Techniques / Q000379:methods*; D003143:Communication Devices for People with Disabilities; D003638:Deafness; D004576:Electromyography; D005868:Gestures; D006225:Hand / Q000502:physiology*; D006801:Humans; D008297:Male; D009068:Movement / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods*; D012813:Sign Language; D014825:Vocabulary
accelerometer; gyroscope; sign language recognition; surface electromyography