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Sign Recognition Technology for the Learning of Hearing Impaired People José Oramas M. 19-10-2009 Information Technology Center Escuela Superior Politécnica del Litoral

Sign Recognition Technology for the Learning of Hearing Impaired People

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Page 1: Sign Recognition Technology for the Learning of Hearing Impaired People

Sign Recognition Technology for the Learning of Hearing Impaired

People

José Oramas M.

19-10-2009

Information Technology CenterEscuela Superior Politécnica del Litoral

Page 2: Sign Recognition Technology for the Learning of Hearing Impaired People

Outline Background

− Problem Statement

− Previous Approaches Proposed Solution

Results

− Methodology

− Discussion Conclusion

Future Work

Page 3: Sign Recognition Technology for the Learning of Hearing Impaired People

Background

Hands as Means of Interaction− Hearing Impaired People

Ecuadorian Hearing Impaired Community− ESL vs. Oralism ===> ESL

− Wide disparity in teacher:student ratio

− No technology-supported learning methodology

Page 4: Sign Recognition Technology for the Learning of Hearing Impaired People

Previous Work Accessible Learning Environments

− eLearning system: Drigas et al. (2005)

− CMS: Drigas et al. (2005)

Sign Language Games− Henderson et al. (2005)

Playware− Especialized toys: Yarosh et al. (2008)

− Augmented Teddy bear: Huang et al. (2008)

Page 5: Sign Recognition Technology for the Learning of Hearing Impaired People

Proposed Solution

Promote the practice of sign language. Easy to use/understand interface. Employ a pointer gesture recognition algorithm

Page 6: Sign Recognition Technology for the Learning of Hearing Impaired People

Testing Prototype

Page 7: Sign Recognition Technology for the Learning of Hearing Impaired People

Testing Methodology Two categories / chapters Controlled test / open test / questionnaire Measured Factors

● Accuracy

● Comfortability

Test users● 12 teachers (age: ~35 )● 10 students (age: ~ 8 , 14 years)

Page 8: Sign Recognition Technology for the Learning of Hearing Impaired People

Results

Accuracy: from 65% to 85%− Different signs and hand sizes

Comfortability− “The glove was too rigid, produced too much pressure

that made it difficult to move the fingers”

Scale Invariant = Users of different heights

Motivation / Acceptance

Page 9: Sign Recognition Technology for the Learning of Hearing Impaired People

Results (cont.)

Group / Self Learning

Page 10: Sign Recognition Technology for the Learning of Hearing Impaired People

Conclusions

Hand gestures recognized as pointer gestures.− Recognition rate of 85% = potential application

Impact on Teaching/Learning− Increases motivation

− Portable sign language classrooms

− Group / Self learning.

Weakness: dataglove

Page 11: Sign Recognition Technology for the Learning of Hearing Impaired People

Future Work Experienced Users (teachers / instructors)

− Study & testing of computer vision based pseudo-posture recognition.

Page 12: Sign Recognition Technology for the Learning of Hearing Impaired People

Future Work (cont.)

Beginners− Alternative datagloves

− GUI enhancements

Promptthe User to sign

ListCategories

AdditionalFeedback

Page 13: Sign Recognition Technology for the Learning of Hearing Impaired People

Sign Recognition Technology for the Learning of Hearing Impaired

People

José Oramas M.

19-10-2009

Information Technology CenterEscuela Superior Politécnica del Litoral