eprintid: 17412 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/01/74/12 datestamp: 2025-03-25 23:30:10 lastmod: 2025-03-25 23:30:12 status_changed: 2025-03-25 23:30:10 type: article metadata_visibility: show creators_name: Ikram, Sunnia creators_name: Bajwa, Imran Sarwar creators_name: Ikram, Amna creators_name: Díez, Isabel de la Torre creators_name: Uc Ríos, Carlos Eduardo creators_name: Kuc Castilla, Ángel Gabriel creators_id: creators_id: creators_id: creators_id: creators_id: carlos.uc@unini.edu.mx creators_id: title: Obstacle Detection and Warning System for Visually Impaired Using IoT Sensors ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uniromana_produccion_cientifica full_text_status: public keywords: Obstacle detection, IoT, sensors, visually impaired, machine learning, android application 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. date: 2025-02 publication: IEEE Access volume: 13 pagerange: 35309-35321 id_number: doi:10.1109/ACCESS.2025.3543299 refereed: TRUE issn: 2169-3536 official_url: http://doi.org/10.1109/ACCESS.2025.3543299 access: open language: en citation: Artículo Materias > Ingeniería <http://repositorio.unini.edu.mx/view/subjects/uneat=5Feng.html> Universidad Europea del Atlántico > Investigación > Producción Científica <http://repositorio.unini.edu.mx/view/divisions/uneatlantico=5Fproduccion=5Fcientifica.html> Universidad Internacional Iberoamericana México > Investigación > Artículos y libros <http://repositorio.unini.edu.mx/view/divisions/uninimx=5Fproduccion=5Fcientifica.html> Universidad de La Romana > Investigación > Producción Científica <http://repositorio.unini.edu.mx/view/divisions/uniromana=5Fproduccion=5Fcientifica.html> Abierto Inglés 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. metadata Ikram, Sunnia; Bajwa, Imran Sarwar; Ikram, Amna; Díez, Isabel de la Torre; Uc Ríos, Carlos Eduardo y Kuc Castilla, Ángel Gabriel mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, carlos.uc@unini.edu.mx, SIN ESPECIFICAR <http://repositorio.unini.edu.mx/id/eprint/17412/1/Obstacle_Detection_and_Warning_System_for_Visually_Impaired_Using_IoT_Sensors.pdf> (2025) Obstacle Detection and Warning System for Visually Impaired Using IoT Sensors. IEEE Access, 13. pp. 35309-35321. ISSN 2169-3536 document_url: http://repositorio.unini.edu.mx/id/eprint/17412/1/Obstacle_Detection_and_Warning_System_for_Visually_Impaired_Using_IoT_Sensors.pdf