Wearable sign language detection system by using convolutional neural network Article Swipe
Raghavendra Reddy
,
Nishanth B. Jain
,
Palash Sharma
,
Vasamsetti Likhitha
,
Mustafa Shihab Assadi
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1063/5.0184057
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1063/5.0184057
Being hearing or speech differently-abled may hinder the communication of an individual, thus the goal of the proposed approach is to reduce the communication gap between a differently-abled and a normal person. We aim to develop a hand Glove that can convert sign language to speech, as well as display text on an App. In addition, the App will also be able to do the reverse and convert speech into sign language. To create a complete sign language translator App that can translate sign language into speech and vice versa. The proposed approach is aimed to solve the problems faced by hearing and speech impaired people and enable better communication for them. It is implemented by using Arduino, Convolutional Neural Network (CNN) and Natural Language Processing (NLP). Arduino will be combined with various sensors like flex sensors to get motion data. The image recognizer will be made by the use of CNN in python using Keras. All these will be integrated into a Streamlit App created using the dart programming language.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1063/5.0184057
- https://pubs.aip.org/aip/acp/article-pdf/doi/10.1063/5.0184057/19668165/020035_1_5.0184057.pdf
- OA Status
- bronze
- Cited By
- 1
- References
- 13
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391790775
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4391790775Canonical identifier for this work in OpenAlex
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https://doi.org/10.1063/5.0184057Digital Object Identifier
- Title
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Wearable sign language detection system by using convolutional neural networkWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
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Raghavendra Reddy, Nishanth B. Jain, Palash Sharma, Vasamsetti Likhitha, Mustafa Shihab AssadiList of authors in order
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https://doi.org/10.1063/5.0184057Publisher landing page
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https://pubs.aip.org/aip/acp/article-pdf/doi/10.1063/5.0184057/19668165/020035_1_5.0184057.pdfDirect link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://pubs.aip.org/aip/acp/article-pdf/doi/10.1063/5.0184057/19668165/020035_1_5.0184057.pdfDirect OA link when available
- Concepts
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Computer science, Wearable computer, Convolutional neural network, Sign language, Artificial intelligence, Sign (mathematics), Speech recognition, Natural language processing, Computer vision, Embedded system, Linguistics, Mathematical analysis, Philosophy, MathematicsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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13Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.The | 90, 141 |
| abstract_inverted_index.aim | 33 |
| abstract_inverted_index.and | 28, 66, 87, 102, 106, 122 |
| abstract_inverted_index.can | 40, 81 |
| abstract_inverted_index.for | 110 |
| abstract_inverted_index.gap | 24 |
| abstract_inverted_index.get | 138 |
| abstract_inverted_index.may | 5 |
| abstract_inverted_index.the | 7, 13, 16, 22, 56, 64, 97, 148, 167 |
| abstract_inverted_index.use | 149 |
| abstract_inverted_index.App. | 53 |
| abstract_inverted_index.able | 61 |
| abstract_inverted_index.also | 59 |
| abstract_inverted_index.dart | 168 |
| abstract_inverted_index.flex | 135 |
| abstract_inverted_index.goal | 14 |
| abstract_inverted_index.hand | 37 |
| abstract_inverted_index.into | 69, 85, 161 |
| abstract_inverted_index.like | 134 |
| abstract_inverted_index.made | 146 |
| abstract_inverted_index.sign | 42, 70, 76, 83 |
| abstract_inverted_index.text | 50 |
| abstract_inverted_index.that | 39, 80 |
| abstract_inverted_index.thus | 12 |
| abstract_inverted_index.vice | 88 |
| abstract_inverted_index.well | 47 |
| abstract_inverted_index.will | 58, 128, 144, 158 |
| abstract_inverted_index.with | 131 |
| abstract_inverted_index.(CNN) | 121 |
| abstract_inverted_index.Being | 0 |
| abstract_inverted_index.Glove | 38 |
| abstract_inverted_index.aimed | 94 |
| abstract_inverted_index.data. | 140 |
| abstract_inverted_index.faced | 99 |
| abstract_inverted_index.image | 142 |
| abstract_inverted_index.solve | 96 |
| abstract_inverted_index.them. | 111 |
| abstract_inverted_index.these | 157 |
| abstract_inverted_index.using | 116, 154, 166 |
| abstract_inverted_index.(NLP). | 126 |
| abstract_inverted_index.Keras. | 155 |
| abstract_inverted_index.Neural | 119 |
| abstract_inverted_index.better | 108 |
| abstract_inverted_index.create | 73 |
| abstract_inverted_index.enable | 107 |
| abstract_inverted_index.hinder | 6 |
| abstract_inverted_index.motion | 139 |
| abstract_inverted_index.normal | 30 |
| abstract_inverted_index.people | 105 |
| abstract_inverted_index.python | 153 |
| abstract_inverted_index.reduce | 21 |
| abstract_inverted_index.speech | 3, 68, 86, 103 |
| abstract_inverted_index.versa. | 89 |
| abstract_inverted_index.Arduino | 127 |
| abstract_inverted_index.Natural | 123 |
| abstract_inverted_index.Network | 120 |
| abstract_inverted_index.between | 25 |
| abstract_inverted_index.convert | 41, 67 |
| abstract_inverted_index.created | 165 |
| abstract_inverted_index.develop | 35 |
| abstract_inverted_index.display | 49 |
| abstract_inverted_index.hearing | 1, 101 |
| abstract_inverted_index.person. | 31 |
| abstract_inverted_index.reverse | 65 |
| abstract_inverted_index.sensors | 133, 136 |
| abstract_inverted_index.speech, | 45 |
| abstract_inverted_index.various | 132 |
| abstract_inverted_index.Arduino, | 117 |
| abstract_inverted_index.Language | 124 |
| abstract_inverted_index.approach | 18, 92 |
| abstract_inverted_index.combined | 130 |
| abstract_inverted_index.complete | 75 |
| abstract_inverted_index.impaired | 104 |
| abstract_inverted_index.language | 43, 77, 84 |
| abstract_inverted_index.problems | 98 |
| abstract_inverted_index.proposed | 17, 91 |
| abstract_inverted_index.Streamlit | 163 |
| abstract_inverted_index.addition, | 55 |
| abstract_inverted_index.language. | 71, 170 |
| abstract_inverted_index.translate | 82 |
| abstract_inverted_index.Processing | 125 |
| abstract_inverted_index.integrated | 160 |
| abstract_inverted_index.recognizer | 143 |
| abstract_inverted_index.translator | 78 |
| abstract_inverted_index.implemented | 114 |
| abstract_inverted_index.individual, | 11 |
| abstract_inverted_index.programming | 169 |
| abstract_inverted_index.Convolutional | 118 |
| abstract_inverted_index.communication | 8, 23, 109 |
| abstract_inverted_index.differently-abled | 4, 27 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.46000000834465027 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.57634449 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |