@inproceedings{e76ecf3938f846d89beebee258041dbd,
title = "Novel feature extraction and classification technique for sensor-based continuous arabic sign language recognition",
abstract = "This paper proposes a novel approach to continuous Arabic Sign Language recognition. We use a dataset which contains 40 sentences composed from 80 sign language words. The dataset is collected using sensor-based gloves. We propose a novel set of features suitable for sensor readings based on covariance, smoothness, entropy and uniformity. We also propose a novel classification approach based on a modified polynomial classifier suitable for sequential data. The proposed classification scheme is modified to take into account the context of the feature vectors prior to classification. This is achieved through the filtering of predicted class labels using median and mode filtering. The proposed work is compared against a vision-based solution. The proposed solution is found to outperform the vision-based solution as it yields an improved sentence recognition rate of 85 \%.",
keywords = "Feature extraction, Pattern classification, Sensor-based gloves, Sign language recognition",
author = "Mohammed Tuffaha and Tamer Shanableh and Khaled Assaleh",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 22nd International Conference on Neural Information Processing, ICONIP 2015 ; Conference date: 09-11-2015 Through 12-11-2015",
year = "2015",
doi = "10.1007/978-3-319-26561-2\_35",
language = "English",
isbn = "9783319265605",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "290--299",
editor = "Sabri Arik and Tingwen Huang and Lai, \{Weng Kin\} and Qingshan Liu",
booktitle = "Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings",
address = "Germany",
}