Indian sign language recognition using wearable sensors and multi-label classification

Sign language recognition is often carried out using hierarchical classification approach to reduce complexity and enhance accuracy. In this paper, mutli-label classification is proposed for categorization of a sign based on its lexical attributes followed by final classification of the sign. Results are presented for classification of 100 isolated signs from the Indian sign language recorded using multiple surface electromyogram and inertial measurement units on both the forearms of 10 different signers. Signals from both the hands are processed in an integrated manner to identify static or dynamic state of the two hands. Moreover, symmetry in the motion of two hands is also utilized for sign categorization using novel features. In the classic tree-based categorization of signs, there is error propagation, which results in a classification error of 6.22%. Whereas in the proposed mutli-label classification approach, error propagation is avoided and the average classification error of 2.73% is observed.