{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T07:57:58Z","timestamp":1774166278122,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T00:00:00Z","timestamp":1752710400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/computers14070284","type":"journal-article","created":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T10:33:47Z","timestamp":1752748427000},"page":"284","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Context-Aware Doorway Alignment and Depth Estimation Algorithm for Assistive Wheelchairs"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-0517-7346","authenticated-orcid":false,"given":"Shanelle","family":"Tennekoon","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Computing & Mathematical Sciences, Curtin University, Bentley, WA 6102, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nushara","family":"Wedasingha","sequence":"additional","affiliation":[{"name":"Department 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 Lanka"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9219-2246","authenticated-orcid":false,"given":"Anuradhi","family":"Welhenge","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Computing & Mathematical Sciences, Curtin University, Bentley, WA 6102, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4662-3214","authenticated-orcid":false,"given":"Nimsiri","family":"Abhayasinghe","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Computing & Mathematical Sciences, Curtin University, Bentley, WA 6102, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1840-9624","authenticated-orcid":false,"given":"Iain","family":"Murray","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Computing & Mathematical Sciences, Curtin University, Bentley, WA 6102, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/0346-251X(95)00005-5","article-title":"Autonomy and motivation a literature review","volume":"23","author":"Dickinson","year":"1995","journal-title":"System"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Atkinson, J. (1991). Autonomy and mental health. Ethical Issues in Mental Health, Springer.","DOI":"10.1007\/978-1-4899-3270-9_7"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wac, K., and Wulfovich, S. (2022). Quantifying Mobility in Quality of Life. Quantifying Quality of Life: Incorporating Daily Life into Medicine, Springer International Publishing.","DOI":"10.1007\/978-3-030-94212-0"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1017\/S0144686X19001296","article-title":"Towards meaningful mobility: A research agenda for movement within and between places in later life","volume":"41","author":"Meijering","year":"2021","journal-title":"Ageing Soc."},{"key":"ref_5","unstructured":"World Health Organization (WHO), and United Nations Children\u2019s Fund (UNICEF) (2025, July 10). Global Report on Assistive Technology. Licence: CC BY-NC-SA 3.0 IGO. Available online: https:\/\/www.who.int\/publications\/i\/item\/9789240074521."},{"key":"ref_6","first-page":"197","article-title":"Spasticity","volume":"2","author":"Marsden","year":"2016","journal-title":"Rheumatol. Rehabil."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"948","DOI":"10.1001\/jama.2014.1397","article-title":"Tremor","volume":"311","author":"Elias","year":"2014","journal-title":"JAMA"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1176\/ajp.33.4.451","article-title":"General paresis","volume":"33","author":"MacDonald","year":"1877","journal-title":"Am. J. Psychiatry"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1080\/10400435.2008.10131933","article-title":"Trends and issues in wheelchair technologies","volume":"20","author":"Cooper","year":"2008","journal-title":"Assist. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1186\/1743-0003-4-45","article-title":"Upper limb impairments associated with spasticity in neurological disorders","volume":"4","author":"Tsao","year":"2007","journal-title":"J. NeuroEng. Rehabil."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1080\/17483107.2020.1854876","article-title":"The global crisis of visual impairment: An emerging global health priority requiring urgent action","volume":"18","author":"Rizzo","year":"2023","journal-title":"Disabil. Rehabil. Assist. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1136\/bjophthalmol-2011-300539","article-title":"Global estimates of visual impairment: 2010","volume":"96","author":"Pascolini","year":"2012","journal-title":"Br. J. Ophthalmol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1080\/09286586.2021.1875009","article-title":"Population-based projection of vision-related disability in australia 2020\u20132060: Prevalence, causes, associated factors and demand for orientation and mobility services","volume":"28","author":"Chang","year":"2021","journal-title":"Ophthalmic Epidemiol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2377","DOI":"10.1016\/j.ophtha.2013.05.025","article-title":"Global prevalence of vision impairment and blindness: Magnitude and temporal trends, 1990\u20132010","volume":"120","author":"Stevens","year":"2013","journal-title":"Ophthalmology"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kim, E.Y. (2016). Wheelchair navigation system for disabled and elderly people. Sensors, 16.","DOI":"10.3390\/s16111806"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1108\/02602281111140029","article-title":"Simple expert systems to improve an ultrasonic sensor-system for a tele-operated mobile-robot","volume":"31","author":"Sanders","year":"2011","journal-title":"Sens. Rev."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zheng, T., Duan, Z., Wang, J., Lu, G., Li, S., and Yu, Z. (2021). Research on distance transform and neural network lidar information sampling classification-based semantic segmentation of 2d indoor room maps. Sensors, 21.","DOI":"10.3390\/s21041365"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Gallo, V., Shallari, I., Carrat\u00f9, M., Laino, V., and Liguori, C. (2024). Design and Characterization of a Powered Wheelchair Autonomous Guidance System. Sensors, 24.","DOI":"10.3390\/s24051581"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Perra, C., Kumar, A., Losito, M., Pirino, P., Moradpour, M., and Gatto, G. (2021). Monitoring Indoor People Presence in Buildings Using Low-Cost Infrared Sensor Array in Doorways. Sensors, 21.","DOI":"10.3390\/s21124062"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Grewal, H., Matthews, A., Tea, R., and George, K. (2017, January 13\u201315). LIDAR-based autonomous wheelchair. Proceedings of the 2017 IEEE Sensors Applications Symposium (SAS), Glassboro, NJ, USA.","DOI":"10.1109\/SAS.2017.7894082"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1109\/MRA.2006.1678144","article-title":"Simultaneous localization and mapping: Part I","volume":"13","author":"Bailey","year":"2006","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"175","DOI":"10.5267\/j.msl.2023.4.004","article-title":"Voice-activated wheelchair: An affordable solution for individuals with physical disabilities","volume":"13","author":"Sahoo","year":"2023","journal-title":"Manag. Sci. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"47","DOI":"10.31181\/jdaic10019022024s","article-title":"Autonomous navigation and obstacle avoidance in smart robotic wheelchairs","volume":"4","author":"Sahoo","year":"2024","journal-title":"J. Decis. Anal. Intell. Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1707","DOI":"10.1177\/0278364910365417","article-title":"Object detection and tracking for autonomous navigation in dynamic environments","volume":"29","author":"Ess","year":"2010","journal-title":"Int. J. Robot. Res."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Qiu, Z., Lu, Y., and Qiu, Z. (2022). Review of ultrasonic ranging methods and their current challenges. Micromachines, 13.","DOI":"10.3390\/mi13040520"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Derry, M., and Argall, B. (2013, January 6\u201310). Automated doorway detection for assistive shared-control wheelchairs. Proceedings of the 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany.","DOI":"10.1109\/ICRA.2013.6630732"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., Marton, Z.C., Blodow, N., Holzbach, A., and Beetz, M. (2009, January 10\u201315). Model-based and learned semantic object labeling in 3D point cloud maps of kitchen environments. Proceedings of the 2009 IEEE\/RSJ International Conference on Intelligent Robots and Systems, St. Louis, MO, USA.","DOI":"10.1109\/IROS.2009.5354759"},{"key":"ref_28","first-page":"3777","article-title":"Detecting and modeling doors with mobile robots","volume":"Volume 4","author":"Anguelov","year":"2004","journal-title":"Proceedings of the IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA\u201904"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lecrosnier, L., Khemmar, R., Ragot, N., Decoux, B., Rossi, R., Kefi, N., and Ertaud, J.Y. (2021). Deep learning-based object detection, localisation and tracking for smart wheelchair healthcare mobility. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18010091"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ju, M., Luo, H., Wang, Z., Hui, B., and Chang, Z. (2019). The application of improved YOLO V3 in multi-scale target detection. Appl. Sci., 9.","DOI":"10.3390\/app9183775"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Bewley, A., Ge, Z., Ott, L., Ramos, F., and Upcroft, B. (2016, January 25\u201328). Simple online and realtime tracking. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, T., Li, J., Jiang, Y., Zeng, M., and Pang, M. (2022). Position detection of doors and windows based on dspp-yolo. Appl. Sci., 12.","DOI":"10.3390\/app122110770"},{"key":"ref_33","unstructured":"Iandola, F., Moskewicz, M., Karayev, S., Girshick, R., Darrell, T., and Keutzer, K. (2014). Densenet: Implementing efficient convnet descriptor pyramids. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial pyramid pooling in deep convolutional networks for visual recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Mochurad, L., and Hladun, Y. (2024). Neural network-based algorithm for door handle recognition using RGBD cameras. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-66864-7"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_37","unstructured":"Hussain, M. (2024). YOLOv5, YOLOv8 and YOLOv10: The Go-To Detectors for Real-time Vision. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"104611","DOI":"10.1016\/j.dsp.2024.104611","article-title":"Enhanced-YOLOv8: A new small target detection model","volume":"153","author":"Wei","year":"2024","journal-title":"Digit. Signal Process."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Sharma, P., Tyagi, R., and Dubey, P. (2024, January 29\u201330). Bridging the Perception Gap A YOLO V8 Powered Object Detection System for Enhanced Mobility of Visually Impaired Individuals. Proceedings of the 2024 First International Conference on Technological Innovations and Advance Computing (TIACOMP), Bali, Indonesia.","DOI":"10.1109\/TIACOMP64125.2024.00028"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Choi, E., Dinh, T.A., and Choi, M. (2025). Enhancing Driving Safety of Personal Mobility Vehicles Using On-Board Technologies. Appl. Sci., 15.","DOI":"10.3390\/app15031534"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/JPROC.2023.3238524","article-title":"Object detection in 20 years: A survey","volume":"111","author":"Zou","year":"2023","journal-title":"Proc. IEEE"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"50534","DOI":"10.1109\/ACCESS.2025.3551686","article-title":"Advancing Object Detection: A Narrative Review of Evolving Techniques and Their Navigation Applications","volume":"13","author":"Tennekoon","year":"2025","journal-title":"IEEE Access"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference On Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Zou, Y., Tan, Y., and Zhou, C. (2024). YOLOv8-seg-CP: A lightweight instance segmentation algorithm for chip pad based on improved YOLOv8-seg model. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-78578-x"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1007\/s42452-021-04588-3","article-title":"Real-time 2D\u20133D door detection and state classification on a low-power device","volume":"3","author":"Lopes","year":"2021","journal-title":"SN Appl. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Kruse, R., Mostaghim, S., Borgelt, C., Braune, C., and Steinbrecher, M. (2022). Multi-layer perceptrons. Computational Intelligence: A Methodological Introduction, Springer.","DOI":"10.1007\/978-3-030-42227-1"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\u201325). Coordinate attention for efficient mobile network design. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Zhu, L., Wang, X., Ke, Z., Zhang, W., and Lau, R.W. (2023, January 17\u201324). Biformer: Vision transformer with bi-level routing attention. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00995"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Xu, W., and Wan, Y. (2024). ELA: Efficient local attention for deep convolutional neural networks. arXiv.","DOI":"10.1007\/s11554-025-01719-6"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"5504005","DOI":"10.1109\/LGRS.2024.3370299","article-title":"Hybrid convolutional and attention network for hyperspectral image denoising","volume":"21","author":"Hu","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_52","unstructured":"Jocher, G. (2020). YOLOv5 by Ultralytics, Zenodo."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2023, January 17\u201324). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_54","unstructured":"Jocher, G., Qiu, J., and Chaurasia, A. (2024, December 28). Ultralytics YOLO. Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1109\/TPAMI.2020.3019967","article-title":"Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer","volume":"44","author":"Ranftl","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Ranftl, R., Bochkovskiy, A., and Koltun, V. (2021, January 10\u201317). Vision transformers for dense prediction. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01196"}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/7\/284\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:11:10Z","timestamp":1760033470000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/7\/284"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,17]]},"references-count":57,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["computers14070284"],"URL":"https:\/\/doi.org\/10.3390\/computers14070284","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,17]]}}}