eprintid: 17412 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/01/74/12 datestamp: 2025-03-25 23:30:11 lastmod: 2025-03-25 23:30:12 status_changed: 2025-03-25 23:30:11 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 Universidad Europea del Atlántico > Investigación > Artículos y libros Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad de La Romana > Investigación > Producción Científica 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 (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.uneatlantico.es/id/eprint/17412/1/Obstacle_Detection_and_Warning_System_for_Visually_Impaired_Using_IoT_Sensors.pdf