Accessible IoT Dashboard Design with AI-Enhanced Descriptions for Visually Impaired Users
The proliferation of the Internet of Things (IoT) has led to an abundance of data streams and real-time dashboards in domains such as smart cities, healthcare, manufacturing, and agriculture. However, many current IoT dashboards emphasize complex visualizations with minimal textual cues, posing sign...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/17/7/274 |
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| author | George Alex Stelea Livia Sangeorzan Nicoleta Enache-David |
| author_facet | George Alex Stelea Livia Sangeorzan Nicoleta Enache-David |
| author_sort | George Alex Stelea |
| collection | DOAJ |
| description | The proliferation of the Internet of Things (IoT) has led to an abundance of data streams and real-time dashboards in domains such as smart cities, healthcare, manufacturing, and agriculture. However, many current IoT dashboards emphasize complex visualizations with minimal textual cues, posing significant barriers to users with visual impairments who rely on screen readers or other assistive technologies. This paper presents AccessiDashboard, a web-based IoT dashboard platform that prioritizes accessible design from the ground up. The system uses semantic HTML5 and WAI-ARIA compliance to ensure that screen readers can accurately interpret and navigate the interface. In addition to standard chart presentations, AccessiDashboard automatically generates long descriptions of graphs and visual elements, offering a text-first alternative interface for non-visual data exploration. The platform supports multi-modal data consumption (visual charts, bullet lists, tables, and narrative descriptions) and leverages Large Language Models (LLMs) to produce context-aware textual representations of sensor data. A privacy-by-design approach is adopted for the AI integration to address ethical and regulatory concerns. Early evaluation suggests that AccessiDashboard reduces cognitive and navigational load for users with vision disabilities, demonstrating its potential as a blueprint for future inclusive IoT monitoring solutions. |
| format | Article |
| id | doaj-art-3591f2841f3f43eda10f1b35a04648a0 |
| institution | Kabale University |
| issn | 1999-5903 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-3591f2841f3f43eda10f1b35a04648a02025-08-20T03:58:26ZengMDPI AGFuture Internet1999-59032025-06-0117727410.3390/fi17070274Accessible IoT Dashboard Design with AI-Enhanced Descriptions for Visually Impaired UsersGeorge Alex Stelea0Livia Sangeorzan1Nicoleta Enache-David2Department of Electronics and Computers, Transilvania University of Brașov, 500036 Brașov, RomaniaDepartment of Mathematics and Informatics, Transilvania University of Brașov, 500036 Brașov, RomaniaDepartment of Mathematics and Informatics, Transilvania University of Brașov, 500036 Brașov, RomaniaThe proliferation of the Internet of Things (IoT) has led to an abundance of data streams and real-time dashboards in domains such as smart cities, healthcare, manufacturing, and agriculture. However, many current IoT dashboards emphasize complex visualizations with minimal textual cues, posing significant barriers to users with visual impairments who rely on screen readers or other assistive technologies. This paper presents AccessiDashboard, a web-based IoT dashboard platform that prioritizes accessible design from the ground up. The system uses semantic HTML5 and WAI-ARIA compliance to ensure that screen readers can accurately interpret and navigate the interface. In addition to standard chart presentations, AccessiDashboard automatically generates long descriptions of graphs and visual elements, offering a text-first alternative interface for non-visual data exploration. The platform supports multi-modal data consumption (visual charts, bullet lists, tables, and narrative descriptions) and leverages Large Language Models (LLMs) to produce context-aware textual representations of sensor data. A privacy-by-design approach is adopted for the AI integration to address ethical and regulatory concerns. Early evaluation suggests that AccessiDashboard reduces cognitive and navigational load for users with vision disabilities, demonstrating its potential as a blueprint for future inclusive IoT monitoring solutions.https://www.mdpi.com/1999-5903/17/7/274Internet of Thingsaccessibilitysemantic HTML5WAI-ARIAartificial intelligenceMulti-Modal Data Representation |
| spellingShingle | George Alex Stelea Livia Sangeorzan Nicoleta Enache-David Accessible IoT Dashboard Design with AI-Enhanced Descriptions for Visually Impaired Users Future Internet Internet of Things accessibility semantic HTML5 WAI-ARIA artificial intelligence Multi-Modal Data Representation |
| title | Accessible IoT Dashboard Design with AI-Enhanced Descriptions for Visually Impaired Users |
| title_full | Accessible IoT Dashboard Design with AI-Enhanced Descriptions for Visually Impaired Users |
| title_fullStr | Accessible IoT Dashboard Design with AI-Enhanced Descriptions for Visually Impaired Users |
| title_full_unstemmed | Accessible IoT Dashboard Design with AI-Enhanced Descriptions for Visually Impaired Users |
| title_short | Accessible IoT Dashboard Design with AI-Enhanced Descriptions for Visually Impaired Users |
| title_sort | accessible iot dashboard design with ai enhanced descriptions for visually impaired users |
| topic | Internet of Things accessibility semantic HTML5 WAI-ARIA artificial intelligence Multi-Modal Data Representation |
| url | https://www.mdpi.com/1999-5903/17/7/274 |
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