Deep-Learning-Based Cognitive Assistance Embedded Systems for People with Visual Impairment
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| Publicat a: | Applied Sciences vol. 15, no. 11 (2025), p. 5887 |
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| Autor principal: | |
| Altres autors: | , |
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MDPI AG
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| Accés en línia: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2076-3417 | ||
| 024 | 7 | |a 10.3390/app15115887 |2 doi | |
| 035 | |a 3217721178 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231338 |2 nlm | ||
| 100 | 1 | |a Ngo Huu-Huy |u Faculty of Information Technology, Thai Nguyen University of Information and Communication Technology, Thai Nguyen 24000, Vietnam; nhhuy@ictu.edu.vn | |
| 245 | 1 | |a Deep-Learning-Based Cognitive Assistance Embedded Systems for People with Visual Impairment | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a For people with vision impairment, various daily tasks, such as independent navigation, information access, and context awareness, may be challenging. Although several smart devices have been developed to assist blind people, most of these devices focus exclusively on navigation assistance and obstacle avoidance. In this study, we developed a portable system for not only obstacle avoidance but also identifying people and their emotions. The core of the developed system is a powerful and portable edge computing device that implements various deep learning algorithms for images captured from a webcam. The user can easily select a function by using a remote control device, and the system vocally reports the results to the user. The developed system has three primary functions: detecting the names and emotions of known people; detecting the age, gender, and emotion of unknown people; and detecting objects. To validate the performance of the developed system, a prototype was constructed and tested. The results reveal that the developed system has high accuracy and responsiveness and is therefore suitable for practical applications as a navigation and social assistive device for people with visual impairment. | |
| 653 | |a Visual impairment | ||
| 653 | |a Embedded systems | ||
| 653 | |a Deep learning | ||
| 653 | |a Smartphones | ||
| 653 | |a Social interaction | ||
| 653 | |a Internet access | ||
| 653 | |a Age | ||
| 653 | |a Computer vision | ||
| 653 | |a Gender | ||
| 653 | |a Sensors | ||
| 653 | |a Classification | ||
| 653 | |a Blindness | ||
| 653 | |a Algorithms | ||
| 653 | |a Facial recognition technology | ||
| 653 | |a Surveillance | ||
| 653 | |a Emotions | ||
| 653 | |a Internet of Things | ||
| 700 | 1 | |a Le, Hung Linh |u Faculty of Engineering and Technology, Thai Nguyen University of Information and Communication Technology, Thai Nguyen 24000, Vietnam; lhlinh@ictu.edu.vn | |
| 700 | 1 | |a Feng-Cheng, Lin |u Department of Information Engineering and Computer Science, Feng Chia University, Taichung 40724, Taiwan | |
| 773 | 0 | |t Applied Sciences |g vol. 15, no. 11 (2025), p. 5887 | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3217721178/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3217721178/fulltextwithgraphics/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3217721178/fulltextPDF/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |