Assistive Navigation System for Blind and Visually Impaired Individuals
DOI:
https://doi.org/10.61453/joit.v2026_0204Keywords:
Assistive Technology, Navigation Systems, Vision Impairment, Artificial TechnologyAbstract
Mobility and independence for visually impaired individuals remain significant challenges worldwide in both indoor and outdoor environments. Recent advances in artificial intelligence (AI), the Internet of Things (IoT), wearable technologies, and sensor systems have enabled the development of innovative assistive solutions to address these challenges. One such solution is the Smart Cap, an affordable wearable device integrating sensors, AI, and IoT technologies to enhance environmental awareness and navigation for blind and visually impaired users. This paper reviews recent research and technological developments supporting the Smart Cap and presents its architecture, functionality, performance, and cost-effectiveness in comparison with existing assistive technologies such as smart canes and smart glasses. The review highlights AI-based object recognition, IoT-enabled data acquisition, sensory feedback mechanisms, and user-centered design approaches that improve navigation accuracy, safety, and usability. Comparative analysis indicates that the Smart Cap offers a balanced combination of hands-free operation, reliable obstacle detection, and low implementation cost while maintaining practical performance. Finally, the paper discusses current challenges and future research directions for advancing intelligent assistive navigation systems.
References
Freitas, M. P., Piai, V. A., Farias, R. H., Fernandes, A. M. R., Rossetto, A. G. M., & Leithardt, V. R. Q. (2022). Artificial intelligence of things applied to assistive technology: A systematic literature review. Sensors, 22(21), 8531. https://doi.org/10.3390/s22218531
Harper, K. A., Kurtzworth-Keen, K., & Marable, M. A. (2017). Assistive technology for students with learning disabilities: A glimpse of the Livescribe pen and its impact on homework completion. Education and Information Technologies, 22(5), 2471–2483. https://doi.org/10.1007/s10639-016-9555-0
Ketter, W., Schroer, K., & Valogianni, K. (2023). Information systems research for smart sustainable mobility: A framework and call for action. Information Systems Research, 34(3), 1045–1065.https://doi.org/10.1287/isre.2022.1167
Pradhan, A., Mehta, K., & Findlater, L. (2018). “Accessibility came by accident”: Use of voice-controlled intelligent personal assistants by people with disabilities. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (Paper 459, pp. 1–13). Association for Computing Machinery. https://doi.org/10.1145/3173574.3174033
Pradhan, A., Mehta, K., & Findlater, L. (2018). “Accessibility came by accident”: Use of voice-controlled intelligent personal assistants by people with disabilities. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (Paper 459, pp. 1–13). Association for Computing Machinery. https://doi.org/10.1145/3173574.3174033
Sun, L., Jiang, X., Ren, H., & Guo, Y. (2020). Edge-cloud computing and artificial intelligence in Internet of Medical Things: Architecture, technology and application. IEEE Access, 8, 101079–101092. https://doi.org/10.1109/ACCESS.2020.2997831
Tapu, R., Mocanu, B., & Zaharia, T. (2017). DEEP-SEE: Joint Object Detection, Tracking and Recognition with Application to Visually Impaired Navigational Assistance. Sensors, 17(11), 2473. https://doi.org/10.3390/s17112473
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