Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode

In this paper, we propose a glove-based Arabic sign language recognition system using a novel technique for sequential data classification. We compile a sensor-based dataset of 40 sentences using an 80-word lexicon. In the dataset, hand movements are captured using two DG5-VHand data gloves. Data labeling is performed using a camera to synchronize hand movements with their corresponding sign language words. Low-complexity preprocessing and feature extraction techniques are applied to capture and emphasize the temporal dependence of the data. Subsequently, a Modified k-Nearest Neighbor (MKNN) approach is used for classification. The proposed MKNN makes use of the context of feature vectors for the purpose of accurate classification. The proposed solution achieved a sentence recognition rate of 98.9%. The results are compared against an existing vision-based approach that uses the same set of sentences. The proposed solution is superior in terms of classification rates while eliminating restrictions of vision-based systems.