Tennekoon, SWedasingha, NWelhenge, AAbhayasinghe, NMurray, I2026-02-192025-07-17Tennekoon, S.; Wedasingha, N.; Welhenge, A.; Abhayasinghe, N.; Murray, I. A Context-Aware Doorway Alignment and Depth Estimation Algorithm for Assistive Wheelchairs. Computers 2025, 14, 284. https://doi.org/10.3390/computers14070284 AMA Style2073431Xhttps://rda.sliit.lk/handle/123456789/4653Navigating through doorways remains a daily challenge for wheelchair users, often leading to frustration, collisions, or dependence on assistance. These challenges highlight a pressing need for intelligent doorway detection algorithm for assistive wheelchairs that go beyond traditional object detection. This study presents the algorithmic development of a lightweight, vision-based doorway detection and alignment module with contextual awareness. It integrates channel and spatial attention, semantic feature fusion, unsupervised depth estimation, and doorway alignment that offers real-time navigational guidance to the wheelchairs control system. The model achieved a mean average precision of 95.8% and a F1 score of 93%, while maintaining low computational demands suitable for future deployment on embedded systems. By eliminating the need for depth sensors and enabling contextual awareness, this study offers a robust solution to improve indoor mobility and deliver actionable feedback to support safe and independent doorway traversal for wheelchair users.enassistive navigationcontext-awaredoorway detectionindoor navigationvision-based navigationwheelchair guidanceYOLOv8 segmentationArticlehttps://doi.org/10.3390/computers14070284