Suche

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"


Abstract:

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.


Downloads:

  • 224  -  (224.pdf, 0 KB)
  • BibTeX  -  (BibTeX.txt, 0 KB)