基于物联网传感器的视障人士障碍物检测与警告系统

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.