Bi-channel Sensor Fusion for Automatic Sign Language Recognition

Jonghwa Kim, Johannes Wagner, Matthias Rehm and Elisabeth André

erschienen 2008 "Proc. IEEE Int. Conf. on Automatic Face and Gesture Recognition"


In this paper, we investigate the mutual-complementary functionality of accelerometer (ACC) and electromyogram (EMG) for recognizing seven word-level sign vocabularies in German Sign Language (GSL). Results are discussed for the single channels and for feature-level fusion for the bichannel sensor data. For the subject-dependent condition, this fusion method proves to be effective. Most relevant features for all subjects are extracted and their universal effectiveness is proven with a high average accuracy for the single subjects. Additionally, results are given for the subject-independent condition, where subjective differences do not allow for high recognition rates. Finally we discuss a problem of feature-level fusion caused by high disparity between accuracies of each single channel classification.


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