Projects per year
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.
Original language | English |
---|---|
Title of host publication | 8th IEEE International Conference on Automatic Face & Gesture Recognition |
Number of pages | 6 |
Publisher | IEEE Computer Society Press |
Publication date | 2008 |
Pages | 1-6 |
Publication status | Published - 2008 |
Keywords
- automatic gesture recognition
- Sensor Fusion
Fingerprint
Dive into the research topics of 'Bi-channel Sensor Fusion for Automatic Sign Language Recognition'. Together they form a unique fingerprint.Projects
- 1 Finished
-
CALLAS: Conveying Affectiveness in Leading-edge Living Adaptive Systems
01/11/2006 → 30/04/2010
Project: Research