Sign Recognition Technology for the Learning of Hearing Impaired People <ul><li>José Oramas M. </li></ul>19-10-2009 Inform...
Outline <ul><li>Background </li></ul><ul><ul><li>Problem Statement </li></ul></ul><ul><ul><li>Previous Approaches </li></u...
Background <ul><li>Hands as Means of Interaction </li></ul><ul><ul><li>Hearing Impaired People </li></ul></ul><ul><li>Ecua...
Previous Work <ul><li>Accessible Learning Environments </li></ul><ul><ul><li>eLearning system: Drigas et al. (2005)‏ </li>...
Proposed Solution <ul><li>Promote the practice of sign language. </li></ul><ul><li>Easy to use/understand interface. </li>...
Testing Prototype
Testing Methodology <ul><li>Two categories / chapters </li></ul><ul><li>Controlled test  / open test / questionnaire </li>...
Results <ul><li>Accuracy:   from 65% to 85% </li></ul><ul><ul><li>Different signs and hand sizes </li></ul></ul><ul><li>Co...
Results  (cont.)‏ <ul><li>Group / Self Learning </li></ul>
Conclusions <ul><li>Hand gestures recognized as pointer gestures. </li></ul><ul><ul><li>Recognition rate of 85%  = potenti...
Future Work <ul><li>Experienced Users  (teachers / instructors)‏ </li></ul><ul><ul><li>Study & testing of computer vision ...
Future Work  (cont.)‏ <ul><li>Beginners </li></ul><ul><ul><li>Alternative datagloves </li></ul></ul><ul><ul><li>GUI enhanc...
Sign Recognition Technology for the Learning of Hearing Impaired People <ul><li>José Oramas M. </li></ul>19-10-2009 Inform...
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Sign Recognition Technology for the Learning of Hearing Impaired People

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Sign Recognition Technology for the Learning of Hearing Impaired People

  1. 1. Sign Recognition Technology for the Learning of Hearing Impaired People <ul><li>José Oramas M. </li></ul>19-10-2009 Information Technology Center Escuela Superior Politécnica del Litoral
  2. 2. Outline <ul><li>Background </li></ul><ul><ul><li>Problem Statement </li></ul></ul><ul><ul><li>Previous Approaches </li></ul></ul><ul><li>Proposed Solution </li></ul><ul><li>Results </li></ul><ul><ul><li>Methodology </li></ul></ul><ul><ul><li>Discussion </li></ul></ul><ul><li>Conclusion </li></ul><ul><li>Future Work </li></ul>
  3. 3. Background <ul><li>Hands as Means of Interaction </li></ul><ul><ul><li>Hearing Impaired People </li></ul></ul><ul><li>Ecuadorian Hearing Impaired Community </li></ul><ul><ul><li>ESL vs. Oralism ===> ESL </li></ul></ul><ul><ul><li>Wide disparity in teacher:student ratio </li></ul></ul><ul><ul><li>No technology-supported learning methodology </li></ul></ul>
  4. 4. Previous Work <ul><li>Accessible Learning Environments </li></ul><ul><ul><li>eLearning system: Drigas et al. (2005)‏ </li></ul></ul><ul><ul><li>CMS: Drigas et al. (2005)‏ </li></ul></ul><ul><li>Sign Language Games </li></ul><ul><ul><li>Henderson et al. (2005)‏ </li></ul></ul><ul><li>Playware </li></ul><ul><ul><li>Especialized toys: Yarosh et al. (2008)‏ </li></ul></ul><ul><ul><li>Augmented Teddy bear: Huang et al. (2008)‏ </li></ul></ul>
  5. 5. Proposed Solution <ul><li>Promote the practice of sign language. </li></ul><ul><li>Easy to use/understand interface. </li></ul><ul><li>Employ a pointer gesture recognition algorithm </li></ul>
  6. 6. Testing Prototype
  7. 7. Testing Methodology <ul><li>Two categories / chapters </li></ul><ul><li>Controlled test / open test / questionnaire </li></ul><ul><li>Measured Factors </li></ul><ul><ul><li>Accuracy </li></ul></ul><ul><ul><li>Comfortability </li></ul></ul><ul><li>Test users </li></ul><ul><ul><li>12 teachers (age: ~35 )‏ </li></ul></ul><ul><ul><li>10 students (age: ~ 8 , 14 years)‏ </li></ul></ul>
  8. 8. Results <ul><li>Accuracy: from 65% to 85% </li></ul><ul><ul><li>Different signs and hand sizes </li></ul></ul><ul><li>Comfortability </li></ul><ul><ul><li>“ The glove was too rigid, produced too much pressure that made it difficult to move the fingers” </li></ul></ul><ul><li>Scale Invariant = Users of different heights </li></ul><ul><li>Motivation / Acceptance </li></ul>
  9. 9. Results (cont.)‏ <ul><li>Group / Self Learning </li></ul>
  10. 10. Conclusions <ul><li>Hand gestures recognized as pointer gestures. </li></ul><ul><ul><li>Recognition rate of 85% = potential application </li></ul></ul><ul><li>Impact on Teaching/Learning </li></ul><ul><ul><li>Increases motivation </li></ul></ul><ul><ul><li>Portable sign language classrooms </li></ul></ul><ul><ul><li>Group / Self learning. </li></ul></ul><ul><li>Weakness: dataglove </li></ul>
  11. 11. Future Work <ul><li>Experienced Users (teachers / instructors)‏ </li></ul><ul><ul><li>Study & testing of computer vision based pseudo-posture recognition. </li></ul></ul>
  12. 12. Future Work (cont.)‏ <ul><li>Beginners </li></ul><ul><ul><li>Alternative datagloves </li></ul></ul><ul><ul><li>GUI enhancements </li></ul></ul>Prompt the User to sign List Categories Additional Feedback
  13. 13. Sign Recognition Technology for the Learning of Hearing Impaired People <ul><li>José Oramas M. </li></ul>19-10-2009 Information Technology Center Escuela Superior Politécnica del Litoral

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