References
- Bhowmick A, Hazarika SM. An insight into assistive technology for the visually impaired and blind people: state-of-the-art and future trends. J Multimodal User Interfaces. 2017;11(2):149–172.
- Patel K, Auton MF, Watkins CL, et al. Assistive device using computer vision and image processing for visually impaired; review and current status. Disabil Rehabil Assist Technol. 2020;1–8. DOI:10.1080/17483107.2020.1786731
- Caspo A, Wersényi G, Jeon M. A survey on hardware and software solutions for multimodal wearable assistive devices targeting the visually impaired. ActaPolytechnicaHungarica. 2016;13(5):39.
- Lahiri A, Chattopadhyay SJ, Basu A. Sparsha: a comprehensive Indian language toolset for the blind. Proceedings of the 7th International ACM SIGACCESS conference on Computers and Accessibility, Baltimore, MD; 2005. p. 114–120.
- Bhandari A, Prasad PWC, Alsadoon A, et al. Object detection and recognition: using deep learning to assist the visually impaired. Disabil Rehabil Assist Technol. 2021;16(3):280–288.
- Ross DA. Implementing assistive technology on wearable computers. IEEE Intell. Syst. 2001;16(3):47–53.
- Bourbakis NG, Kavraki D. NovemberAn intelligent assistant for navigation of visually impaired people. In: Proceedings 2nd Annual IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2001). Bethesda (MD): IEEE; 2001. p. 230–235.
- Giudice NA, Legge GE. Blind navigation and the role of technology. In: The engineering handbook of smart technology for aging, disability, and independence. Hoboken (NJ): John Wiley & Sons; 2008. p. 479–500.
- Kassim AM, Jaafar HI, Azam MA, et al. Design and development of navigation system by using RFID technology. In: 2013 IEEE 3rd International Conference on System Engineering and Technology. Shah Alam (Malaysia): IEEE; 2013. p. 258–262.
- Liu S, Ma W, Schalow D, et al. Improving web access for visually impaired users. IT Prof. 2004;6(4):28–33.
- Hasselbring TS, Bausch ME. Assistive technologies for reading. Educ Leadersh. 2005;63(4):72.
- Dasgupta T, Anuj A, Sinha M, et al. DecemberVoiceMail architecture in desktop and mobile devices for the Blind people. In: 2012 4th International Conference on Intelligent Human Computer Interaction (IHCI). Kharagpur (India): IEEE; 2012. p. 1–6.
- Saha B, Bhowmick B, Sinha A. An embedded solution for visually impaired. IEEE 13th International Symposium on Consumer Electronics. Kyoto, Japan: IEEE; 2009. p. 467–471.
- Legge GE, Beckmann PJ, Tjan BS, et al. Indoor navigation by people with visual impairment using a digital sign system. PLoS One. 2013;8(10):e76783.
- Vacher M, Lecouteux B, Portet F. Multichannel automatic recognition of voice command in a multi-room smart home: an experiment involving seniors and users with visual impairment. In: Interspeech 2014. Singapore; 2014. p. 1008–1012.
- Liu Y, Bacon J, Wilson-Hinds R. On smart-care services: studies of visually impaired users in living contexts. In: First International Conference on the Digital Society (ICDS’07). Guadeloupe (French Caribbean): IEEE; 2007. p. 32–32.
- Domingo MC. An overview of the internet of things for people with disabilities. J Netw Comput Appl. 2012;35(2):584–596.
- Sarji DK. Handtalk: assistive technology for the deaf. Computer. 2008;41(7):84–86.
- Lartz MN, Stoner JB, Stout LJ. Perspectives of assistive technology from deaf students at a hearing university. Assist Technol Outcomes Benefits. 2008;5(1):72–91.
- Sriram N, Nithiyanandham M. A hand gesture recognition-based communication system for silent speakers. In: 2013 International Conference on Human Computer Interactions (ICHCI). Chennai (India): IEEE. 2013. p. 1–5.
- Ghule S, Chavaan M. 2021. Implementation of hand gesture recognition system to aid deaf-dumb people. In: Advances in signal and data processing. Springer. p. 183–194.
- Tandel PS, Dubey S. Sign language recognition using image-based hand gesture recognition techniques. VIVA-Tech Int J Res Innov. 2021;1(4):1–6.
- Jreige C, Patel R, Bunnell HT. VocaliD: personalizing text-to-speech synthesis for individuals with severe speech impairment. In: Proceedings of the 11th international ACM SIGACCESS conference on Computers and accessibility. Pittsburgh (PA): ACM; 2009. p. 259–260.
- Young V, Mihailidis A. Difficulties in automatic speech recognition of dysarthric speakers and implications for speech-based applications used by the elderly: a literature review. Assist Technol. 2010;22(2):99–112.
- Hawley MS, Cunningham SP, Green PD, et al. A voice-input voice-output communication aid for people with severe speech impairment. IEEE Trans Neural Syst Rehabil Eng. 2013;21(1):23–31.
- Mulfari D, Meoni G, Marini M, et al. Machine learning assistive application for users with speech disorders. Appl Soft Comput. 2021;103:107147.
- Neeraja MY, Reddy DS, Kalpana J, et al. An advanced braille system-communication device for blind-deaf people. Dogo Rangsang Res J. 2021;8(14):1–6.
- Ramachandran S, Gururaj D, Pallavi KN, et al. Text to Braille converting communication device for the visual and hearing impaired persons. In: IEEE International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India; 2021. p. 1–5.
- Johnson S, Gao G, Johnson T, et al. An adaptive, affordable, open-source robotic hand for deaf and deaf-blind communication using tactile american sign language. In: 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Mexico; 2021. p. 1–6.
- Bourbakis N, Esposito A, Kabraki D. Multi-modal interfaces for interaction-communication between hearing and visually impaired individuals: problems and issues. In: 19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2007). Vol. 2. Patras (Greece): IEEE. 2007. p. 522–530.
- Walters S. Toward an accessible pedagogy: dis/ability, multimodality, and universal design in the technical communication classroom. Tech Commun Q. 2010;19(4):427–454.
- Moustakas K, Tzovaras D, Dybkjaer L, et al. Using modality replacement to facilitate communication between visually and hearing-impaired people. IEEE Multimedia Mag. 2011;18(2):26–37.
- Karpov A, Ronzhin A. A universal assistive technology with multimodal input and multimedia output interfaces. In: International conference on universal access in human-computer interaction. Crete (Greece): Springer; 2014. p. 369–378.
- Eide AH, Oderud T. Assistive technology in low-income countries. In: Disability international development. New York (NY): Springer. 2009. p. 149–160.
- American Sign Language. [Online]. 2022 May 19. Available from: https://www.ai-media.tv/sign-language-alphabets-from-around-the-world/
- Coleman MB, Cramer ES, Park Y, et al. Art educators’ use of adaptations, assistive technology, and special education supports for students with physical, visual, severe and multiple disabilities. J Dev Phys Disabil. 2015;27(5):637–660.
- Zhou L, Ajuwon PM, Smith DW, et al. Assistive technology competencies for teachers of students with visual impairments: a national study. J Visual Impairment Blindness. 2012;106(10):656–665.
- Borg J, Larsson S, Östergren PO. The right to assistive technology: for whom, for what, and by whom? Disabil Soc. 2011;26(2):151–167.
- Albawi S, Mohammed TA, Al-Zawi S. Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology (ICET). Antalya (Turkey): IEEE; 2017. p. 1–6.
- O’Shea K, Nash R. 2015. An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458.
- Goodfellow I, Bengio Y, Courville A, et al. 2016. Deep learning. Vol. 1, No. 2. Cambridge: MIT press.