A Context-Aware Doorway Alignment and Depth Estimation Algorithm for Assistive Wheelchairs
Navigating 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 detect...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/14/7/284 |
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| author | Shanelle Tennekoon Nushara Wedasingha Anuradhi Welhenge Nimsiri Abhayasinghe Iain Murray |
| author_facet | Shanelle Tennekoon Nushara Wedasingha Anuradhi Welhenge Nimsiri Abhayasinghe Iain Murray |
| author_sort | Shanelle Tennekoon |
| collection | DOAJ |
| description | Navigating 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. |
| format | Article |
| id | doaj-art-e467da263abc49598850bf653dc2b4b6 |
| institution | DOAJ |
| issn | 2073-431X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-e467da263abc49598850bf653dc2b4b62025-08-20T03:08:10ZengMDPI AGComputers2073-431X2025-07-0114728410.3390/computers14070284A Context-Aware Doorway Alignment and Depth Estimation Algorithm for Assistive WheelchairsShanelle Tennekoon0Nushara Wedasingha1Anuradhi Welhenge2Nimsiri Abhayasinghe3Iain Murray4School of Electrical Engineering, Computing & Mathematical Sciences, Curtin University, Bentley, WA 6102, AustraliaDepartment of Electrical and Electronic Engineering, Center of Excellence in Informatics (CIET), Electronics & Transmission, Faculty of Engineering, Sri Lanka Institute of Information Technology, Malabe 10115, Sri LankaSchool of Electrical Engineering, Computing & Mathematical Sciences, Curtin University, Bentley, WA 6102, AustraliaSchool of Electrical Engineering, Computing & Mathematical Sciences, Curtin University, Bentley, WA 6102, AustraliaSchool of Electrical Engineering, Computing & Mathematical Sciences, Curtin University, Bentley, WA 6102, AustraliaNavigating 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.https://www.mdpi.com/2073-431X/14/7/284assistive navigationcontext-awaredoorway detectionindoor navigationvision-based navigationwheelchair guidance |
| spellingShingle | Shanelle Tennekoon Nushara Wedasingha Anuradhi Welhenge Nimsiri Abhayasinghe Iain Murray A Context-Aware Doorway Alignment and Depth Estimation Algorithm for Assistive Wheelchairs Computers assistive navigation context-aware doorway detection indoor navigation vision-based navigation wheelchair guidance |
| title | A Context-Aware Doorway Alignment and Depth Estimation Algorithm for Assistive Wheelchairs |
| title_full | A Context-Aware Doorway Alignment and Depth Estimation Algorithm for Assistive Wheelchairs |
| title_fullStr | A Context-Aware Doorway Alignment and Depth Estimation Algorithm for Assistive Wheelchairs |
| title_full_unstemmed | A Context-Aware Doorway Alignment and Depth Estimation Algorithm for Assistive Wheelchairs |
| title_short | A Context-Aware Doorway Alignment and Depth Estimation Algorithm for Assistive Wheelchairs |
| title_sort | context aware doorway alignment and depth estimation algorithm for assistive wheelchairs |
| topic | assistive navigation context-aware doorway detection indoor navigation vision-based navigation wheelchair guidance |
| url | https://www.mdpi.com/2073-431X/14/7/284 |
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