Akademik Veri Yönetim Sistemi
IEEE Transactions on Intelligent Transportation Systems, 2026 (SCI-Expanded, Scopus)
The advancement in portable and lightweight sensor technology made the inertial data from daily activities widely accessible, contributing to the interest of tracking these movements or detecting characteristic patterns in these movements. This study focuses on detecting the crosswalk-crossing behavior of visually impaired individuals, which are observed to differ from those of normal sighted people. We hypothesize that a tactile search of the crosswalk endpoint using a cane, a hesitation at the endpoints, or a particular steady walking while crossing are reflected as a unique series of readings in the inertial data. Crosswalk-crossings generate rare and relatively long-duration patterns that regular walking actions, making it challenging to determine which features of the sensory data to focus on. We propose a specially tuned learning framework for detecting crosswalk-crossings using Deep Convolutional Neural Networks, which are known to be effective at data analysis involving temporal hierarchies. Considering also the use cases of the detected crosswalk-crossings, such as correction in the pedestrian positioning systems, we emphasize a precision-focused training with weighted F-scores and a related loss function that minimize false detections. Our findings show that our approach successfully detects real-world crosswalk-crossings with high precision and acceptable recall values. We also show that the models are capable of performing similarly on the data from different devices they are not trained with.