@article{uneatlantico17412,
           month = {Febrero},
           title = {Obstacle Detection and Warning System for Visually Impaired Using IoT Sensors},
            year = {2025},
         journal = {IEEE Access},
           pages = {35309--35321},
          volume = {13},
          author = {Sunnia Ikram and Imran Sarwar Bajwa and Amna Ikram and Isabel de la Torre D{\'i}ez and Carlos Eduardo Uc R{\'i}os and {\'A}ngel Gabriel Kuc Castilla},
        keywords = {Obstacle detection, IoT, sensors, visually impaired, machine learning, android application},
             url = {http://repositorio.uneatlantico.es/id/eprint/17412/},
        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 Na{\"i}ve 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.}
}