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Introduction to Large-Scale Distributed Computing in Smart Healthcare Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Wearable Computers for Sign Language Recognition

Authors : Jian Wu, Roozbeh Jafari

Published in: Handbook of Large-Scale Distributed Computing in Smart Healthcare

Publisher: Springer International Publishing

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Abstract

A Sign Language Recognition (SLR) system translates signs performed by deaf individuals into text/speech in real time. Low cost sensor modalities, inertial measurement unit (IMU) and surface electromyography (sEMG), are both useful to detect hand/arm gestures. They are capable of capturing signs and are complementary to each other for recognizing signs. In this book chapter, we propose a wearable system for recognizing American Sign Language (ASL) in real-time, fusing information from an inertial sensor and sEMG sensors. The best subset of features from a wide range of well-studied features is selected using an information gain based feature selection approach. Four popular classification algorithms are evaluated for 80 commonly used ASL signs on four subjects. With the selected feature subset and a support vector machine classifier, our system achieves 96.16 and 85.24% average accuracies for intra-subject and intra-subject cross session evaluation respectively. The significance of adding sEMG for American Sign Language recognition is explored and the best channel of sEMG is highlighted.

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previous chapter Large-Scale Innovations and Approaches for Community Healthcare Support in Developing Nations
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Metadata
Title
Wearable Computers for Sign Language Recognition
Authors
Jian Wu
Roozbeh Jafari
Copyright Year
2017
Book
Handbook of Large-Scale Distributed Computing in Smart Healthcare

Print ISBN: 978-3-319-58279-5

Electronic ISBN: 978-3-319-58280-1

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-58280-1_14

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