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Malaysian sign gesture recognition with deep learning and myo armband

Q325.73.N87 2018

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Main Author: Nur Adrynna Nisha Mohd Rashid
Format: text::Final Year Project
Language:English US
Published: 2024
Subjects:
Deep learning
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spelling my.uniten.dspace-317292024-03-31T02:00:51Z Malaysian sign gesture recognition with deep learning and myo armband Nur Adrynna Nisha Mohd Rashid Deep learning Q325.73.N87 2018 This thesis reports are a development of an improved method for hearing and speaking impaired community. There are a large scale of hearing and speaking impaired community in the whole world. They had a problem to communicate with normal people who did not know any sign language. That is why sign language is important to this community. To get a better sign language a good sign gesture is needed. In this project, the aim is to use a method of deep learning to recognize the sign gesture that be perform and translate it into text. In previous studies, there are many methods be use to solve this problem including using different type of gesture recognition device. However, in this thesis our approach is use a friendly wearable device to recognize the Malaysian Sign Language gesture using the data that be capture from the device. The data that be capture from the device is based on two data type which are gestural data that is Electromyography and spatial data that include Accelerometer, Gyroscope and Magnetometer (orientation). There are 12 different gesture be performed that represents many types of finger and hand gesture either in static gesture or dynamic gesture. Whereas for the sign recognition, a Long Short-Term Memory (LSTM) approach is be use which is a kind of Recurrent Neural Network (RNN) approach. After that, the dataset is preprocessed and reshaped to make sure it in the same length without have to skip any sample from the dataset. Then, LSTM will supervise the sign identification as of supervise learning in the network. In this project, there are four way of result that will be evaluated. First one is only using the electromyography sensor data. Second way using only gyroscope sensor data. Third way is the combination of gyroscope and accelerometer sensor data. Lastly is only using accelerometer sensor data. Other than that, it will be compared with the Gated Recurrent Unit (GRU). Throughout the model implementation result that have been conducted, it shows a high accuracy of 98.3% in the electromyography sensor data. By that, it can be identified as a successful achievement approach. 2024-03-29T09:27:10Z 2024-03-29T09:27:10Z 2018 Resource Types::text::Final Year Project https://irepository.uniten.edu.my/handle/123456789/31729 en_US application/pdf
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language English US
topic Deep learning
spellingShingle Deep learning
Nur Adrynna Nisha Mohd Rashid
Malaysian sign gesture recognition with deep learning and myo armband
description Q325.73.N87 2018
format Resource Types::text::Final Year Project
author Nur Adrynna Nisha Mohd Rashid
author_facet Nur Adrynna Nisha Mohd Rashid
author_sort Nur Adrynna Nisha Mohd Rashid
title Malaysian sign gesture recognition with deep learning and myo armband
title_short Malaysian sign gesture recognition with deep learning and myo armband
title_full Malaysian sign gesture recognition with deep learning and myo armband
title_fullStr Malaysian sign gesture recognition with deep learning and myo armband
title_full_unstemmed Malaysian sign gesture recognition with deep learning and myo armband
title_sort malaysian sign gesture recognition with deep learning and myo armband
publishDate 2024
_version_ 1806424545699037184
score 13.214268

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