Recognizing Postures in Vietnamese Sign Language With MEMS Accelerometers

Bui, T.D.; Nguyen, Long Thang

doi:10.1109/jsen.2007.894132

Cited by 58 publications

(27 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In [4], researchers developed a system of classifying symbols in Greek sign language using the energy obtained from bodies with biosensors (EMG) and the assembled data from an acceleration sensor mounted on the arm. For VSL, the researchers in [5] used gloves which are combined with sensors to identify 23 character gestures in the Vietnamese alphabet. These methods focused on the medical field as well as controlling, they thus still have a limited capability to identify the actual sign language gestures.…”

Section: A Sensor-based Approachmentioning

confidence: 99%

Dynamic Gesture Classification for Vietnamese Sign Language Recognition

Vo¹,

Huynh²,

Doan³

et al. 2017

ijacsa

View full text Add to dashboard Cite

Abstract-This paper presents an approach of feature extraction and classification for recognizing continuous dynamic gestures corresponding to Vietnamese Sign Language (VSL). Input data are captured by the depth sensor of a Microsoft Kinect, which is almost not affected by the light of environment. In detail, each gesture is represented by a volume corresponding to a sequence of depth images. The feature extraction stage is performed by dividing such volume into a 3D grid of same-size blocks in which each one is then converted into a scalar value. This step is followed by the process of classification. The wellknown method Support Vector Machine (SVM) is employed in this work, and the Hidden Markov Model (HMM) technique is also applied in order to provide a comparison on recognition accuracy. Besides, a dataset of 3000 samples corresponding to 30 dynamic gestures in VSL was created by 5 volunteers. The experiments on this dataset to validate the approach and that shows the promising results with average accuracy up to 95%.

show abstract

Section: A Sensor-based Approachmentioning

confidence: 99%

Dynamic Gesture Classification for Vietnamese Sign Language Recognition

Vo¹,

Huynh²,

Doan³

et al. 2017

ijacsa

View full text Add to dashboard Cite

show abstract

“…Their method was based on a virtual stereo vision system, using one camera and gloves with a specially designed colour pattern to indicate the 5 separate fingers, palm and back. Bui et al [13] developed a new SLR system for the Vietnamese Sign Language. They utilized an extra sensor which was mounted at the back side of the handin order to obtain better recognition .…”

Section: Fig22 Hand Gesture Experimental Setup With Four Electrodes mentioning

confidence: 99%

Review of Literature for the Development of Indian Sign Language Recognition System

Dour¹,

Sharma²

2015

IJCA

View full text Add to dashboard Cite

“…Böylece programlamada if-then-else ifadeleri kullanılmıştır. Bu sistem 23 harften 20'sinde % 100 doğrulukla 'U' harfi için ise % 79'luk bir doğrulukla test edilmiştir [4]. 2013 yılında Gunesekaran ve Manikandan adlı bilim adamları da veri eldiveni tekniğini kullanarak bir işaret dili tercüme sistemi geliştirmişlerdir.…”

Section: Introductionunclassified

Lati̇s Tabanli Anlam Çözümlenmesi̇ İle Türkçe İşaret Di̇li̇ Tercüme Si̇stemi̇

Firat¹,

Uğurlu²

2018

Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi

View full text Add to dashboard Cite

ÖZGünlük hayatta insanlar; fikirlerini, düşüncelerini ve yaşadıklarını çevrelerindeki insanlara iletmek için birbirleriyle etkileşirler. İşitme ve konuşma engelli insanlar ise çevreleriyle bu etkileşimi sağlayamazlar. Başkalarıyla iletişim kurmak için işaret dilini kullanırlar. İşaret dili ise, işitme ve konuşma engellilerin kendi aralarında el hareketleri ve yüz mimikleri ile iletişim kurmalarını sağlayan ülkeden ülkeye değişen evrensel olmayan bir dildir. Bu anlamda yapılan çalışmanın amacı, işitme ve konuşma bozukluğu olan kişilerle normal insanlar arasındaki iletişimi sağlayan Türkçe İşaret Dili tercüme sistemini geliştirmektir. Önerilen bu sistemle işaret dilini gösteren el hareketleri, Kinect aygıtı yardımıyla yakalanarak Kontur Analizinde kullanılan algoritmalarla çözümlenmiştir. Çözümlenen görüntülerden elde edilen kelimelerin gerçek anlamları, Biçimsel Kavram Analizi Kuramı çerçevesinde hazırlanan tematik rol latisleriyle bulunmuştur. Gerçek anlamları bulunan bu kelimeler, içinde geçtikleri cümlelerle birlikte bilgisayar ekranında görüntülenmiştir. Böylece bu sistemle, engelli bir kişinin kitlesel bir kalabalıkla iletişim kurabilmesi sağlanmıştır. Ayrıca geliştirilen bu sistemle Türkçe'nin anlamsal çözümlenmesine de katkı sağlanması hedeflenmiştir.Anahtar Kelimeler: Biçimsel kavram analizi, işaret dili tanımlama, kinect, kontur analizi, tematik rol latisleri TURKISH SIGN LANGUAGE TRANSLATION SYSTEM WITH A LATTICE-BASED SEMANTIC ANALYSIS ABSTRACTIn daily life people interact with each other to communicate their ideas, thoughts and experiences to the people around them. On the other hand, people with hearing and speech impairments are not capable of communicating with their environment in the given sense. Thus, they use sign language to communicate with others. Sign language is a non-universal language that changes depending on the country in which it's being used and enables people with hearing and speech impairments to communicate with other people through hand gestures and facial expressions. In this sense, the aim of the study is to develop Turkish Sign Language translation system that enables communication between people with speech and hearing impairments and healthy people. With this proposed system, the hand movements showing sign language are captured with the help of Kinect device and analyzed with the algorithms used in Contour Analysis. Actual meanings of the words obtained from the analyzed images were found thematic role lattices that are prepared in the frame of Formal Concept Analysis theory. The words of which actual meanings were found are displayed with the sentences in which they were used. Therefore, thanks to this system it is ensured that a disabled person can communicate with a mass crowd. The developed system also aims to contribute to the semantic analysis of the Turkish Language.

show abstract

Recognizing Postures in Vietnamese Sign Language With MEMS Accelerometers

Cited by 58 publications

References 17 publications

Dynamic Gesture Classification for Vietnamese Sign Language Recognition

Dynamic Gesture Classification for Vietnamese Sign Language Recognition

Review of Literature for the Development of Indian Sign Language Recognition System

Lati̇s Tabanli Anlam Çözümlenmesi̇ İle Türkçe İşaret Di̇li̇ Tercüme Si̇stemi̇

Contact Info

Product

Resources

About