Abstract

Sign Language Recognition (SLR) system is a novel method that allows hard of hearing to communicate with general society. In this study, American Sign Language (ASL) recognition system was proposed by using the surface Electromyography (sEMG). The objective of this study is to recognize the American Sign Language alphabet letters and allow users to spell words and sentences. For this purpose, sEMG data are acquired from subject right forearm for twenty-seven American Sign Language gestures of twenty-six English alphabets and one for home position. Time and frequency domain (band power) information used in the feature extraction process. As a classification method, Support Vector Machine and Ensemble Learning algorithm were used and their performances are compared with tabulated results.

In conclusion, the results of this study show that sEMG signal can be used for SLR systems.

Library of Congress Subject Headings

Optical pattern recognition; Image processing--Digital techniques; American Sign Language--Translating--Data processing; Machine learning; Electromyography

Publication Date

12-2015

Document Type

Thesis

Student Type

Graduate

Degree Name

Electrical Engineering (MS)

Department, Program, or Center

Electrical Engineering (KGCOE)

Advisor

Ferat Sahin

Advisor/Committee Member

Eli Saber

Advisor/Committee Member

Sildomar T. Monteiro

Comments

Physical copy available from RIT's Wallace Library at TA1650 .S38 2015

Campus

RIT – Main Campus

Plan Codes

EEEE-MS

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