Novel Feature Extraction and Classification Technique for Sensor-Based Continuous Arabic Sign Language Recognition
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 covaria...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , |
| Format: | article |
| Published: |
2015
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/11073/21379 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1864513434497843200 |
|---|---|
| author | Tuffaha, Mohammed |
| author2 | Shanableh, Tamer Assaleh, Khaled |
| author2_role | author author |
| author_facet | Tuffaha, Mohammed Shanableh, Tamer Assaleh, Khaled |
| author_role | author |
| dc.creator.none.fl_str_mv | Tuffaha, Mohammed Shanableh, Tamer Assaleh, Khaled |
| dc.date.none.fl_str_mv | 2015 2021-03-18T08:56:09Z 2021-03-18T08:56:09Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Tuffaha M., Shanableh T., Assaleh K. (2015) Novel Feature Extraction and Classification Technique for Sensor-Based Continuous Arabic Sign Language Recognition. In: Arik S., Huang T., Lai W., Liu Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science, vol 9492. Springer, Cham. https://doi.org/10.1007/978-3-319-26561-2_35 978-3-319-26561-2 http://hdl.handle.net/11073/21379 10.1007/978-3-319-26561-2_35 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | Springer |
| dc.relation.none.fl_str_mv | https://doi.org/10.1007/978-3-319-26561-2_35 |
| dc.subject.none.fl_str_mv | Sign language recognition Feature extraction Sensor-based gloves Pattern classification |
| dc.title.none.fl_str_mv | Novel Feature Extraction and Classification Technique for Sensor-Based Continuous Arabic Sign Language Recognition |
| dc.type.none.fl_str_mv | Peer-Reviewed Preprint info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | 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 %. |
| format | article |
| id | aus_fe0b686de1ca018cd8e9df7853a3e0e7 |
| identifier_str_mv | Tuffaha M., Shanableh T., Assaleh K. (2015) Novel Feature Extraction and Classification Technique for Sensor-Based Continuous Arabic Sign Language Recognition. In: Arik S., Huang T., Lai W., Liu Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science, vol 9492. Springer, Cham. https://doi.org/10.1007/978-3-319-26561-2_35 978-3-319-26561-2 10.1007/978-3-319-26561-2_35 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/21379 |
| publishDate | 2015 |
| publisher.none.fl_str_mv | Springer |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Novel Feature Extraction and Classification Technique for Sensor-Based Continuous Arabic Sign Language RecognitionTuffaha, MohammedShanableh, TamerAssaleh, KhaledSign language recognitionFeature extractionSensor-based glovesPattern classificationThis 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 %.Springer2021-03-18T08:56:09Z2021-03-18T08:56:09Z2015Peer-ReviewedPreprintinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfTuffaha M., Shanableh T., Assaleh K. (2015) Novel Feature Extraction and Classification Technique for Sensor-Based Continuous Arabic Sign Language Recognition. In: Arik S., Huang T., Lai W., Liu Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science, vol 9492. Springer, Cham. https://doi.org/10.1007/978-3-319-26561-2_35978-3-319-26561-2http://hdl.handle.net/11073/2137910.1007/978-3-319-26561-2_35en_UShttps://doi.org/10.1007/978-3-319-26561-2_35oai:repository.aus.edu:11073/213792024-08-22T12:08:42Z |
| spellingShingle | Novel Feature Extraction and Classification Technique for Sensor-Based Continuous Arabic Sign Language Recognition Tuffaha, Mohammed Sign language recognition Feature extraction Sensor-based gloves Pattern classification |
| status_str | publishedVersion |
| title | Novel Feature Extraction and Classification Technique for Sensor-Based Continuous Arabic Sign Language Recognition |
| title_full | Novel Feature Extraction and Classification Technique for Sensor-Based Continuous Arabic Sign Language Recognition |
| title_fullStr | Novel Feature Extraction and Classification Technique for Sensor-Based Continuous Arabic Sign Language Recognition |
| title_full_unstemmed | Novel Feature Extraction and Classification Technique for Sensor-Based Continuous Arabic Sign Language Recognition |
| title_short | Novel Feature Extraction and Classification Technique for Sensor-Based Continuous Arabic Sign Language Recognition |
| title_sort | Novel Feature Extraction and Classification Technique for Sensor-Based Continuous Arabic Sign Language Recognition |
| topic | Sign language recognition Feature extraction Sensor-based gloves Pattern classification |
| url | http://hdl.handle.net/11073/21379 |