An IoT-Based Smart Cane for Blind Individuals with Emergency Alert System

Blindness continues to limit the safety and mobility of many individuals. Traditional White canes can help people navigate space; however‚ they do not provide real-time monitoring‚ emergency alert‚ or environmental detection․ This study carried out the design‚ development‚ and testing of an IoT-based Smart Cane with obstacle and water detection‚ GPS-based location monitoring‚ SOS emergency alert‚ and Multilingual Voice Assistance for Blind Individuals․ The Spiral model was used for this study as a research design․ The prototype was implemented with an ESP32 microcontroller‚ sensors‚ GPS module‚ Firebase Realtime Database‚ and Apache Cordova framework for the Guardian Monitoring Application․ Purposive and snowball sampling methods were used for the sampling of blind respondents‚ guardians‚ and non-blind respondents from the province of Lanao del Norte in Mindanao region․ The IoT-Based Smart Cane was evaluated using USE Questionnaires and the ISO/IEC 25010 system quality using the Five-Point Likert Scale‚ the Smart Cane was found to have the ability to detect obstacles and wet surfaces‚ send SOS emergency alert notification‚ provide real time GPS location‚ and deliver multilingual voice assistance and vibration feedbacks․ Overall‚ results showed high satisfaction ratings for usability‚ functionality‚ accuracy and effectiveness of the system‚ and showed that merging IoT and assistive material into a single assistive device improved safety‚ navigation and mobility of blind individuals․

IoT-Based Smart Cane; Blindness; Obstacle Detection; GPS Monitoring; SOS Emergency Alert

1. Ali, Z. A. (2023). Design and evaluation of two obstacle detection devices for visually impaired people. Journal of Engineering Research, 11(3), 100–105. https://doi.org/10.1016/j.jer.2023.100132 [Google Scholar] [Crossref]

2. Bharadwaj, A. S. (n.d.). Smart Blind Stick For Detection and Avoidance of Obstacle. 8(9). [Google Scholar] [Crossref]

3. Bheemireddi, V. R. C., Pasupuleti, S. T., Gannamani, K. S., Bandaru, G., & Mula, V. S. S. N. (2024). Obstacle Detection and Fire Detection for Visually Imapired using Smart Blind Stick. 2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), 1075–1078. https://doi.org/10.1109/ICAAIC60222.2024.10575684 [Google Scholar] [Crossref]

4. Chung, S., & Son, J.-W. (2020). Visual perception in autism spectrum disorder: A review of neuroimaging studies. Journal of the Korean Academy of Child & Adolescent Psychiatry, 31(3), 105–120. [Google Scholar] [Crossref]

5. Cleveland Clinic. (2022). Blindness. Cleveland Clinic. Blindness Articl [Google Scholar] [Crossref]

6. Farooq, M. S., Shafi, I., Khan, H., Díez, I. D. L. T., Breñosa, J., Espinosa, J. C. M., & Ashraf, I. (2022). IoT Enabled Intelligent Stick for Visually Impaired People for Obstacle Recognition. Sensors, 22(22), 8914. https://doi.org/10.3390/s22228914 [Google Scholar] [Crossref]

7. Lund, A.M. (2001) Measuring Usability with the USE Questionnaire. STC Usability SIG Newsletter, 8:2 [Google Scholar] [Crossref]

8. Malla, S., Sahu, P. K., Patnaik, S., & Biswal, A. K. (2023). Obstacle Detection and Assistance for Visually Impaired Individuals Using an IoT-Enabled Smart Blind Stick. Revue d’Intelligence Artificielle, 37(3), 783–794. https://doi.org/10.18280/ria.370327 [Google Scholar] [Crossref]

9. N, R. (2024). Design and Implementation of Smart Blind Stick for Obstacle Detection and Navigation System. Journal of Electrical Systems, 20(2s), 1277–1289. https://doi.org/10.52783/jes.1770 [Google Scholar] [Crossref]

10. Natarajan, V., M, Y., & Canessane, A. (2022). IoT based Smart Stick with Automated Obstacle Detector for Blind People. 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI), 536–540. https://doi.org/10.1109/ICOEI53556.2022.9777174 [Google Scholar] [Crossref]

11. Pathak, A., Singh, A. P., Sharma, H., Kumar, S., & Saini, Y. (2021). Rapid Response Smart Blind Stick with Obstacle Detection Using Arduino. International Journal of Technical Research & Science, 14–17. https://doi.org/10.30780/IJTRS.V06.I08.003 [Google Scholar] [Crossref]

12. Pal, S. K. (2025, July 11). What is Spiral Model in Software Engineering? GeeksforGeeks. https://www.geeksforgeeks.org/software-engineering/software-engineering-spiral-model/ [Google Scholar] [Crossref]

13. Palachi, E. (2025, July 28). System architecture diagram basics & best practices. vFunction. https://vfunction.com/blog/architecture-diagram-guide/ [Google Scholar] [Crossref]

14. Pranata, S. (2025). IoT-based smart cane with sensors and GPS as a modern medical solution to help safe and independent mobility for the visually impaired in daily life. Create the Future Design Contest. https://contest.techbriefs.com/2025/entries/medical/13417 [Google Scholar] [Crossref]

15. Raghu, N., Kumaran, S. Y., Rajeshwari, D., Shetty, G. S., Kumar, B. M. H., Kumar, D. A., & Kannanugo, N. (2024, March 31). Design and implementation of smart blind stick for obstacle detection and navigation system. Journal of Electrical Systems, 20(2 S), Article 1770. [Google Scholar] [Crossref]

16. Sandman, A. (2025, June 2). Use cases: Diagram & examples (Updated 2024). Inflectra. https://www.inflectra.com/Ideas/Topic/Use-Cases.aspx [Google Scholar] [Crossref]

17. Shinagawa Lasik & Aesthetics. (2025, May 29). Perfect vision and eye health in the Philippines. Shinagawa. [Google Scholar] [Crossref]

18. Simran, S. (2025, April 14). Spiral model for software development: A risky-driven model. BDTASK. https://www.bdtask.com/blog/spiral-model-for-software-development [Google Scholar] [Crossref]

19. Vennila, P., Alamelu Mangayarkarasi, V., Vinayakan, K., & Gnana Raja, G. R. (2025). Smart IoT navigation system for visually impaired individuals: Improving safety and independence with advanced obstacle detection. International Journal of Computational Research and Development, 10(1), 17–23. [Google Scholar] [Crossref]

20. World Health Organization. (2019). World report on vision. World Health Organization. https://iris.who.int/handle/10665/328717 [Google Scholar] [Crossref]

• What the Desert Fathers Teach Data Scientists: Ancient Ascetic Principles for Ethical Machine-Learning Practice
• Comparative Analysis of Some Machine Learning Algorithms for the Classification of Ransomware
• Comparative Performance Analysis of Some Priority Queue Variants in Dijkstra’s Algorithm
• Transfer Learning in Detecting E-Assessment Malpractice from a Proctored Video Recordings.
• Dual-Modal Detection of Parkinson’s Disease: A Clinical Framework and Deep Learning Approach Using NeuroParkNet