Ikram, Sunnia and Bajwa, Imran Sarwar and Ikram, Amna and Díez, Isabel de la Torre and Uc Ríos, Carlos Eduardo and Kuc Castilla, Ángel Gabriel UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, carlos.uc@unini.edu.mx, UNSPECIFIED (2025) Obstacle Detection and Warning System for Visually Impaired Using IoT Sensors. IEEE Access, 13. pp. 35309-35321. ISSN 2169-3536
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Abstract
Ensuring safe and independent mobility for visually impaired individuals requires efficient obstacle detection systems. This study introduces an innovative smart knee glove, integrating machine learning technologies for real-time obstacle detection and alerting. The system is equipped with ultrasonic sensor, PIR sensor and a buzzer, with data processing managed by an Arduino Uno microcontroller. To enhance detection accuracy, multiple machine learning algorithms including Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest (RF) and Gaussian Naïve Bayes (GNB) are utilized. A novel Voting Classifier ensemble method is proposed, effectively combining the strengths of these classifiers to maximize performance. Rigorous cross-fold validation ensures robust evaluation under varying conditions. Experimental results demonstrates that the system achieves an impressive 98.34% detection accuracy within a 4-meter range, with high precision, recall and F1 scores. These findings underscore the system’s reliability and potential to empower visually impaired users with safer, more autonomous navigation, marking a significant advancement in obstacle detection technologies.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Obstacle detection, IoT, sensors, visually impaired, machine learning, android application |
| Subjects: | Subjects > Engineering |
| Divisions: | Europe University of Atlantic > Research > Scientific Production Ibero-american International University > Research > Scientific Production University of La Romana > Research > Scientific Production |
| Depositing User: | Sr Bibliotecario |
| Date Deposited: | 25 Mar 2025 08:48 |
| Last Modified: | 25 Mar 2025 08:48 |
| URI: | http://repositorio.funiber.org/id/eprint/17412 |
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