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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.
Originalsprog | Engelsk |
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Titel | 8th IEEE International Conference on Automatic Face & Gesture Recognition |
Antal sider | 6 |
Forlag | IEEE Computer Society Press |
Publikationsdato | 2008 |
Sider | 1-6 |
Status | Udgivet - 2008 |
Fingeraftryk
Dyk ned i forskningsemnerne om 'Bi-channel Sensor Fusion for Automatic Sign Language Recognition'. Sammen danner de et unikt fingeraftryk.Projekter
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CALLAS: Conveying Affectiveness in Leading-edge Living Adaptive Systems
01/11/2006 → 30/04/2010
Projekter: Projekt › Forskning