relation: http://repositorio.unini.edu.mx/id/eprint/17412/
canonical: http://repositorio.unini.edu.mx/id/eprint/17412/
title: Obstacle Detection and Warning System for Visually Impaired Using IoT Sensors
creator: Ikram, Sunnia
creator: Bajwa, Imran Sarwar
creator: Ikram, Amna
creator: Díez, Isabel de la Torre
creator: Uc Ríos, Carlos Eduardo
creator: Kuc Castilla, Ángel Gabriel
subject: Ingeniería
description: 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
type: Artículo
type: PeerReviewed
format: text
language: en
rights: cc_by_4
identifier: http://repositorio.unini.edu.mx/id/eprint/17412/1/Obstacle_Detection_and_Warning_System_for_Visually_Impaired_Using_IoT_Sensors.pdf
identifier:   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     
relation: http://doi.org/10.1109/ACCESS.2025.3543299
relation: doi:10.1109/ACCESS.2025.3543299
language: en