Development of magnetic-sensor-based hand gesture recognition system for sign language

Sign language recognition is essential for the automatic translation of sign languages to enable communication for hearing-impaired people. This work proposes a system based on multiple magnetic sensors for recognizing hand gestures related to sign language alphabets. In particular, a magnetic detection system consisting of six magnetic sensor nodes measures the orientation of fingers and palms. A deep learning classification algorithm processes the measured orientation data. Experimental tests validate the proposed system and classification method. The results show that the proposed method provides close to 100% classification accuracy for 26 sign language alphabets under laboratory conditions. Thus, the feasibility of the proposed gesture recognition system for automatic translation of sign language alphabets is proved.