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View all- Zhang XHuang XDing YLong LLi WXu X(2024)Advancements in Smart Wearable Mobility Aids for Visual Impairments: A Bibliometric Narrative ReviewSensors10.3390/s2424798624:24(7986)Online publication date: 14-Dec-2024
Image Recognition Tools for Blind and Visually Impaired Users: An Emphasis on the Design Considerations
Article No.: 1, Pages 1 - 21
Abstract
The existing digital image recognition technologies available for blind individuals are commercially accessible but still at an immature stage, necessitating enhancements in their capabilities. Areas requiring improvement in current image recognition tools for the visually impaired encompass information accuracy, information adequacy, appropriateness of information for blind individuals, functional sufficiency and ergonomic suitability. This research endeavours to explore technology employing an inclusive approach to overcome limitations inherent in current digital image recognition technologies for the visually impaired. To streamline the research process, the initial phase involves a critical evaluation of deficiencies present in existing image recognition technologies for blind/visually impaired users through primary and secondary investigations. This strategic evaluation aims to identify key deficiencies, guiding assistive technology developers in focusing their efforts. Simultaneously, a survey is conducted to establish a comprehensive checklist of usability features and requirements for the proposed technology, aligning with the ISO 9241-110 standard. The overarching goal of this research is to address the question, ‘What design considerations should be taken into account in designing image recognition technology for blind and visually impaired people?’ The research outcomes are intended to establish a standard for designers of digital products catering to blind/visually impaired users, fostering improved awareness and shaping attitudes toward individuals with visual disabilities in the development of image recognition software.
References
[1]
Adam Zewe. 2022. Making Data Visualization More Accessible for Blind and Low-Vision Individuals. Retrieved from https://news.mit.edu/2022/data-visualization-accessible-blind-0602
[2]
Alexiou Gus. 2021. Envision Smart Glasses – A Game-Changer In Helping Blind People Master Their Environment. Forbes. Retrieved from https://www.forbes.com/sites/gusalexiou/2021/01/28/envision-ai-glasses–a-game-changer-in-helping-blind-people-master-their-environment/
[3]
F. Al-Qunaieer. 2014. Automated resolution selection for image segmentation. arXiv:1605.06820. Retrieved from https://arxiv.org/abs/1605.06820
[4]
Andy Nguye and P. Liamputtong. (Eds.). 2021. Digital inclusion. In Handbook of Social Inclusion. Springer. Retrieved from https://link.springer.com/referenceworkentry/10.1007/978-3-030-48277-0_14-1#citeas
[5]
S. Antol, A. Agrawal, J. Lu, M. Mitchell, C. L. Zitnick, D. Batra, and D. Parikh. 2015. VQA: Visual question answering. arXiv:1505.00468. Retrieved from https://arxiv.org/abs/1505.00468
[6]
AppAdvice. 2017. Aipoly Vision: Sight for Blind & Visually Impaired. Retrieved from https://appadvice.com/app/aipoly-vision-sight-for-blind-visually-impaired/1069166437
[7]
E. R. Babbie. 2016. The Practice of Social Research (14th ed.), Cengage Learning, Belmont. Retrieved from https://www.scirp.org/reference/ReferencesPapers.aspx?ReferenceID=2439585
[8]
Mark S. Baldwin, and Jennifer Mankoff, and Bonnie Nardi, and Gillian Hayes. 2020. An activity centered approach to nonvisual computer interaction. ACM Transactions on Computer-Human Interaction, 27, 2 (2020), 1–27. DOI:
[9]
S. Banerjee, K. Deb, A. Das, and R. Bag. 2021. A survey on the use of adaptive learning techniques towards learning personalization. In Handbook of Research on Modern Educational Technologies, Applications, and Management. s.l.:IGI Global, 790–808.
[10]
Bhanuka Gamage, Thanh-Toan Do, Nicholas Seow Chiang Price, Arthur Lowery, and Kim Marriott. 2023. What do Blind and Low-Vision People Really Want from Assistive Smart Devices? Comparison of the Literature with a Focus Study. ACM, New York, NY. DOI:
[11]
Bill Holton. 2013. A Review of the TapTapSee, CamFind, and Talking Goggles Object Identification Apps for the iPhone. American Foundation for the Blind. Retrieved from https://www.afb.org/aw/14/7/15675
[12]
A. Bochkovskiy, C. Y. Wang, and H. Y. M. Liao. 2020. YOLOv4: Optimal speed and accuracy of object detection. arXiv:2004.10934. Retrieved from https://arxiv.org/pdf/2004.10934
[13]
IEEE. 2023. IEEE Standard for Robustness Testing and Evaluation of Artificial Intelligence (AI)-Based Image Recognition Service. IEEE Std 3129–2023, 1–34.
[14]
Á. A. Cabrera, A. Perer, and J. I. Hong. 2023. Improving human-AI collaboration with descriptions of AI behavior. Proceedings of the ACM on Human-Computer Interaction 7, CSCW1 (2023), 1–21.
[15]
Chandrika Jayant, Hanjie Ji, Samuel White, Jeffrey P. Bigham. 2011. Supporting blind photography. In Proceedings of the 13th International ACM SIGACCESS Conference on Computers and Accessibility. ACM, New York, NY. Retrieved from https://cs.rochester.edu/hci/pubs/pdfs/supporting-blind-photography.pdf
[16]
M. Luo and K. Zhang. 2014. A hybrid approach combining extreme learning machine and sparse representation for image classification. Engineering Applications of Artificial Intelligence 27 (2014), 228–235. DOI: https://doi.org/10.1016/j.engappai.2013.05.012
[17]
Gamage Bhanuka, Thanh-Toan Do, Nicholas Seow Chiang Price, Arthur Lowery, and Kim Marriott. 2023. What do blind and low-vision people really want from assistive smart devices? Comparison of the literature with a focus study. In Proceedings of the 25th International ACM SIGACCESS Conference on Computers and Accessibility, 1–21.
[18]
R. Girshick, J. Donahue, T. Darrell, and J. Malik, 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 580–587. Retrieved from https://arxiv.org/abs/1311.2524
[19]
T. Hayashi, D. Cimr, H. Fujita, and R. Cimler. 2023. Image entropy equalization: A novel preprocessing technique for image recognition tasks. Information Sciences 647 (2023). DOI: https://doi.org/10.1016/j.ins.2023.119539
[20]
M. A. Hersh. 2018. Mobility technologies for blind, partially sighted and deafblind people: Design issues. In Mobility of Visually Impaired People. E. Pissaloux and R. Velazquez (Eds.), Springer, Cham, 377–409.
[21]
G. E. Hinton, I. Sutskever, and A. Krizhevsky. 2017. ImageNet classification with deep convolutional neural networks. Communications of the ACM 60, 6 (2017), 84–90.
[22]
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam. 2017. MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861. Retrieved from http://export.arxiv.org/abs/1704.04861
[23]
ISO. 2006. ISO 9241-110:2006 Ergonomics of Human-System Interaction—Part 110: Dialogue Principles. Retrieved September 19, 2023 from https://www.iso.org/standard/38009.html
[24]
ISO. 2018. ISO 9999:2016 - Assistive Products for Persons with Disability—Classification and Terminology. ISO. Retrieved from https://www.iso.org/standard/50982.html
[25]
ISO. 2019. ISO/IEC 20071-21:2015 - Information Technology—User Interface Component Accessibility—Part 21: Guidance on Audio Descriptions. International Organization for Standardization. Retrieved from https://www.iso.org/standard/70485.html
[26]
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi. 2016. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779–788. DOI: https://doi.org/10.1109/CVPR.2016.91
[27]
Jack Lenstrohm. 2019. TapTapSee App Review. Blindresources. Retrieved December 13, 2022 from https://blindresources.org/2019/07/12/taptapsee-app-review/
[28]
K. He, X. Zhang, S. Ren, and J. Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. DOI: https://doi.org/10.1109/CVPR.2016.90
[29]
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, and A. C. Berg. 2016. SSD: Single shot multi-box detector. In Proceedings of the European Conference on Computer Vision. Springer, Cham, 21–37. Retrieved from https://arxiv.org/abs/1512.02325
[30]
Y. H. Liu. 2018. Feature extraction and image recognition with convolutional neural networks. Journal of Physics: Conference Series 1087 (2018), 062032. DOI: https://doi.org/10.1088/1742-6596/1087/6/062032
[31]
LookTel. 2009. LookTel Recognizer. Retrieved December 11, 2022 from https://www.iaccessibility.com/apps/low-vision/index.cgi/product?ID=44
[32]
LookTel. 2009. What is LookTel? Retrieved September 16, 2023 from http://www.looktel.com/
[33]
I. Sutskever, O. Vinyals, and Q. V. Le. 2014. Sequence to sequence learning with neural networks. arXiv:1409.3215 Retrieved from https://arxiv.org/abs/1409.3215
[34]
Marcus Klasson and Cheng Zhang. 2020. Using Variational Multi-view Learning for Classification of Grocery Items. CellPress. Retrieved December 13, 2022 from https://www.cell.com/patterns/pdfExtended/S2666-3899(20)30191-4
[35]
Microsoft. 2023. How Seeing AI Works. Retrieved February 18, 2023 from https://www.microsoft.com/en-us/seeing-ai/how-it-works
[36]
OpenAI. 2023. GPT-4V(ision) System Card.
[37]
Raz Ben. 2017. Aipoly Reviews - Pros & Cons 2022 - Product Hunt. Retrieved December 13, 2022 from https://www.producthunt.com/products/aipoly/reviews
[38]
J. Redmon and A. Farhadi. 2018. YOLOv3: An Incremental Improvement. Retrieved from https://arxiv.org/pdf/1804.02767.pdf
[39]
Robert Sanders. 2020. Digital Inclusion. IRISS. Retrieved from https://www.iriss.org.uk/sites/default/files/2020-04/iriss_esss_outline_digital_inclusion_09042020_0.pdf
[40]
S. Hochreiter and J. Schmidhuber. 1997. Long short-term memory. Neural Computation 9, 8 (1997) 1735–1780. DOI: https://doi.org/10.1162/neco.1997.9.8.1735
[41]
S. Saeed, M. Rohde, and V. Wulf. 2013. Designing an assistive learning aid for writing acquisition: A challenge for heterogeneous groups. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2033–2042. Retrieved from https://www.scirp.org/journal/paperinformation.aspx?paperid=123700
[42]
Sanni Siltanen. 2012. Theory and applications of marker-based augmented reality. Vuorimiehentie, Finland, JULKAISIJA – UTGIVARE – PUBLISHER. Retrieved from https://www.vttresearch.com/sites/default/files/pdf/science/2012/S3.pdf
[43]
Teresa Correa and Isabel Pavez. 2016. Digital inclusion in rural areas: A qualitative exploration of challenges faced by people from isolated communities. Journal of Computer-Mediated Communication 21, 3 (2016), 247–263. Retrieved from https://academic.oup.com/jcmc/article/21/3/247/4065369
[44]
Tushar P. Ghate, Sukruti Y. Khairnar, Santosh A. Bangar, J. P. Chavan. 2015. Customizable object detection using smartphone. International Journal of Advanced Research in Computer and Communication Engineering 4, 2 (2015), 1–4. Retrieved from https://ijarcce.com/wp-content/uploads/2015/03/IJARCCE6A.pdf
[45]
Varsha Sharma, Chaitanya Sharma, Sahil Jain, and Siddhant Jain. 2019. Assitance application for visually impaired - vision. International Journal of Scientific Research and Engineering Development 2, 6 (2019), 1–4. Retrieved from http://www.ijsred.com/volume2/issue6/IJSRED-V2I6P52.pdf
[46]
RNIB. 2022. Wearable Technology: Smart Glasses and Head Mounted Cameras. Royal National Institute of Blind People. Retrieved December 13, 2022 from https://www.rnib.org.uk/living-with-sight-loss/assistive-aids-and-technology/tech-support-and-information/wearable-technology-smart-glasses-and-head-mounted-cameras/
[47]
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86, 11 (1998), 2278–2324. DOI: https://doi.org/10.1109/5.726791
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Publication History
Published: 08 January 2025
Online AM: 29 October 2024
Accepted: 16 September 2024
Revised: 09 August 2024
Received: 06 December 2023
Published in TACCESS Volume 18, Issue 1
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View all- Zhang XHuang XDing YLong LLi WXu X(2024)Advancements in Smart Wearable Mobility Aids for Visual Impairments: A Bibliometric Narrative ReviewSensors10.3390/s2424798624:24(7986)Online publication date: 14-Dec-2024
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