Bi-channel sensor fusion for automatic sign language 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 fustion 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 subjectindependent 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.

• Type: Conference
• Year: 2008
• Where: FGR
• Authors: Jonghwa Kim, Johannes Wagner, Matthias Rehm, Elisabeth André
• Keywords: Biometrics, FGR 2008, German Sign Language, Single Channel, Word-level Sign Vocabularies