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• W3173996913 abstract "Hand gesture recognition is a crucial task for the automated translation of sign language, which enables communication for the deaf. This work proposes the usage of a magnetic positioning system for recognizing the static gestures associated with the sign language alphabet. In particular, a magnetic positioning system, which is comprised of several wearable transmitting nodes, measures the 3D position and orientation of the fingers within an operating volume of about 30 × 30 × 30 cm, where receiving nodes are placed at known positions. Measured position data are then processed by a machine learning classification algorithm. The proposed system and classification method are validated by experimental tests. Results show that the proposed approach has good generalization properties and provides a classification accuracy of approximately 97% on 24 alphabet letters. Thus, the feasibility of the proposed gesture recognition system for the task of automated translation of the sign language alphabet for fingerspelling is proven." @default.
• W3173996913 created "2021-07-05" @default.
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• W3173996913 date "2021-06-17" @default.
• W3173996913 modified "2023-09-30" @default.
• W3173996913 title "Gesture Recognition of Sign Language Alphabet Using a Magnetic Positioning System" @default.
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• W3173996913 doi "https://doi.org/10.3390/app11125594" @default.
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