Analytical Study And Overview On Glove Based Indian Sign Language Interpretation Technique

Das, Prasenjit; De, Riju; Paul, Soumi; Chowdhury, Mainak; Neogi, Biswarup

doi:10.1049/cp.2015.1650

Cited by 6 publications

(13 citation statements)

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“…Finger tilt can be detected by using different methods, as shown in the literature. Flex sensor ( Figure 6 ), which determines the amount of finger curvature used by a wide number of researchers and developers, is the most common of these kinds of sensors [ 21 , 37 , 38 , 39 , 40 ]. The Flex sensor technology is based on resistive carbon elements.…”

Section: Methodsmentioning

confidence: 99%

“…Several Arduino boards are available on the market such as Arduino Nano, Uno, Mega, etc. ; for instance, Arduino Uno (Arduino, Italy), ( Figure 13 c) is based on the ATmega328P microcontroller and has 14 digital inputs/outputs, 6 analog inputs, a 16 MHz quartz crystal, and USB connection [ 6 , 8 , 21 , 37 , 56 , 57 , 58 , 59 ]. Odroid XU4 (Hardkernel2, Anyang, South Korea) ( Figure 13 d), produced by Hardkernel2, uses a Samsung Exynos5 Octa 5410 processor that consists of 4 CPU cores each with Cortex™-A15 cores; it was used for the development of the data glove [ 59 ].…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, it is possible to train this technique to distinguish both static and dynamic gestures, as well as posture classification, based on the data obtained from the data glove [ 41 , 43 , 54 , 70 , 71 ]. Long-term Fuzzy Logic has been used in many fields that need human decision-making; one of these areas is recognition of sign language [ 4 , 21 , 42 , 72 ]. Likewise, there is another useful algorithm; in fact, it falls within the scope of machine learning employed to provide accurate and less complicated classification through dimensionality reduction with improved clustering, which is known as Linear Discriminant Analysis (LDA) [ 46 , 61 ].…”

Section: Methodsmentioning

confidence: 99%

“…The aim was to understand the options and gaps available in this field to support researchers by providing valuable insights into technological environments. The steps taken in this study to provide Systematic Literature Review and Systematic Mapping Study are in accordance with the steps explained in the David Moher paper [ 16 ] and other research papers [ 11 , 17 , 18 , 19 , 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

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A Review on Systems-Based Sensory Gloves for Sign Language Recognition State of the Art between 2007 and 2017

Ahmed

Zaidan

et al. 2018

Sensors

186

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Loss of the ability to speak or hear exerts psychological and social impacts on the affected persons due to the lack of proper communication. Multiple and systematic scholarly interventions that vary according to context have been implemented to overcome disability-related difficulties. Sign language recognition (SLR) systems based on sensory gloves are significant innovations that aim to procure data on the shape or movement of the human hand. Innovative technology for this matter is mainly restricted and dispersed. The available trends and gaps should be explored in this research approach to provide valuable insights into technological environments. Thus, a review is conducted to create a coherent taxonomy to describe the latest research divided into four main categories: development, framework, other hand gesture recognition, and reviews and surveys. Then, we conduct analyses of the glove systems for SLR device characteristics, develop a roadmap for technology evolution, discuss its limitations, and provide valuable insights into technological environments. This will help researchers to understand the current options and gaps in this area, thus contributing to this line of research.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Review on Systems-Based Sensory Gloves for Sign Language Recognition State of the Art between 2007 and 2017

Ahmed

Zaidan

et al. 2018

Sensors

186

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“…Telah banyak karya penelitian yang diarahkan dan dilakukan untuk mengembangkan pengenalan bahasa isyarat yang berbeda-beda. Beberapa penelitian tersebut adalah American Sign Language (ASL) [2], Malaysian Sign Language [3], Indian Sign Language (ISL) [4] dan Arabic Sign Language (ArSL) [5]. Penelitian bahasa isyarat lainnya dengan melakukan pendekatan berbasis citra dan juga sensor, dimana algoritma bahasa isyarat dibagi menjadi tiga bagian yaitu pengenalan berdasarkan huruf, kata dan kalimat.…”

Section: Introductionunclassified

Penggunaan Dual Sensor Leap Motion Controller untuk Sistem Isyarat Bahasa Indonesia (SIBI)

Nugraha

Murti²,

Khotimah³

2019

INFORMAL

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Bahasa isyarat adalah bahasa yang dibentuk dengan kombinasi gerakan jari, tangan, tubuh serta ekspresi wajah yang digunakan oleh para penyandang disabilitas seperti tunarungu dan tunawicara. Salah satu pengenalan bahasa isyarat tersebut adalah pengenalan dengan menggunakan teknologi sensor Leap Motion Controller (LMC). Akan tetapi pada penerapannya, sensor LMC tidak selalu dapat mengenali semua jari dengan baik. Hal ini dikarenakan pembentukan dari bahasa isyarat yang saling bersentuhan antar jari, jari yang dilipat, tersembunyi ataupun terhalang oleh jari lainnya. Sehingga pada penelitian ini diusulkan penggunaan dual sensor LMC yang bertujuan untuk mencegah dan meminimalisir adanya kesalahan dalam mengenal bahasa isyarat dari jari yang dibentuk. Tahapan proses yang dilakukan adalah mendesain tata letak sensor LMC dan mengkombinasikan data fitur dari masing-masing sensor LMC baik yang bersifat statis dan dinamis yang kemudian diklasifikasikan menggunakan metode L-GCNN. Dari hasil pengujian yang dilakukan, penggunaan dual sensor LMC ini dapat mengenal isyarat SIBI yang diberikan. Skema yang terbaik adalah dengan mengacu pada tata letak multi sensor yang saling berhadapan membentuk sudut 120 derajat dengan tingkat akurasi sebesar 86,42%, dengan rata-rata akurasi dari keseluruhan skenario sebesar 84,36%. Adanya desain tata letak sensor LMC dan kombinasi penggabungan data fitur masing-masing sensor dapat meningkatkan akurasi pengenalan dengan rata-rata peningkatan mencapai 5,35%.

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Real-time sign language framework based on wearable device: analysis of MSL, DataGlove, and gesture recognition

Ahmed

Zaidan

et al. 2021

Soft Comput

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Analytical Study And Overview On Glove Based Indian Sign Language Interpretation Technique

Cited by 6 publications

References 0 publications

A Review on Systems-Based Sensory Gloves for Sign Language Recognition State of the Art between 2007 and 2017

A Review on Systems-Based Sensory Gloves for Sign Language Recognition State of the Art between 2007 and 2017

Penggunaan Dual Sensor Leap Motion Controller untuk Sistem Isyarat Bahasa Indonesia (SIBI)

Real-time sign language framework based on wearable device: analysis of MSL, DataGlove, and gesture recognition

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