How to cite this paper
Sahoo, S & Choudhury, B. (2024). Exploring the use of computer vision in assistive technologies for individuals with disabilities: A review.Journal of Future Sustainability, 4(3), 133-148.
Refrences
Abraham, L., Mathew, N. S., George, L., & Sajan, S. S. (2020, June). VISION-wearable speech based feedback system for the visually impaired using computer vision. In 2020 4th International Conference on Trends in Electronics and Informat-ics (ICOEI)(48184) (pp. 972-976). IEEE.
Akilan, T., Wu, Q. J., & Zhang, H. (2018). Effect of fusing features from multiple DCNN architectures in image classifica-tion. IET Image Processing, 12(7), 1102-1110.
Amtmann, D., McMullen, K., Bamer, A., Fauerbach, J. A., Gibran, N. S., Herndon, D, Schneider, J.C, Koalske, K.,Holavanahalli, R., & Miller, A. C. (2020). National institute on disability, independent living, and rehabilitation re-search burn model system: review of program and database. Archives of physical medicine and rehabilitation, 101(1), S5-S15.
Andrich, R. (2013). Service Delivery Systems for Assistive Technology in Europe: A Position Paper. In Assistive Technolo-gy: From Research to Practice (pp. 247-253). IOS Press.
Antoniadi, A. M., Du, Y., Guendouz, Y., Wei, L., Mazo, C., Becker, B. A., & Mooney, C. (2021). Current challenges and fu-ture opportunities for XAI in machine learning-based clinical decision support systems: a systematic review. Applied Sciences, 11(11), 5088.
Arigo, D., Lobo, A. F., Ainsworth, M. C., Baga, K., & Pasko, K. (2022). Development and initial testing of a personalized, adaptive, and socially focused web tool to support physical activity among women in midlife: multidisciplinary and us-er-centered design approach. JMIR Formative Research, 6(7), e36280.
Busaeed, S., Mehmood, R., Katib, I., & Corchado, J. M. (2022). LidSonic for Visually Impaired: Green Machine Learning-Based Assistive Smart Glasses with Smart App and Arduino. Electronics, 11(7), 1076.
Calabrò, R. S., Cerasa, A., Ciancarelli, I., Pignolo, L., Tonin, P., Iosa, M., & Morone, G. (2022). The Arrival of the Metaverse in Neurorehabilitation: Fact, Fake or Vision?. Biomedicines, 10(10), 2602.
Chai, J., Zeng, H., Li, A., & Ngai, E. W. (2021). Deep learning in computer vision: A critical review of emerging techniques and application scenarios. Machine Learning with Applications, 6, 100134.
Chen, C., Wang, Y., Niu, J., Liu, X., Li, Q., & Gong, X. (2021). Domain knowledge powered deep learning for breast cancer diagnosis based on contrast-enhanced ultrasound videos. IEEE Transactions on Medical Imaging, 40(9), 2439-2451.
de Belen, R. A. J., Bednarz, T., Sowmya, A., & Del Favero, D. (2020). Computer vision in autism spectrum disorder re-search: a systematic review of published studies from 2009 to 2019. Translational psychiatry, 10(1), 333.
Faria Oliveira, O. D., Carvalho Gonçalves, M., de Bettio, R. W., & Pimenta Freire, A. (2022). A qualitative study on the needs of visually impaired users in Brazil for smart home interactive technologies. Behaviour & Information Technology, 1-29.
Fazelpour, S., & Danks, D. (2021). Algorithmic bias: Senses, sources, solutions. Philosophy Compass, 16(8), e12760.
Finco, M. D., Dantas, V. R., & dos Santos, V. A. (2023). Exergames, Artificial Intelligence and Augmented Reality: Connec-tions to Body and Sensorial Experiences. In Augmented Reality and Artificial Intelligence: The Fusion of Advanced Tech-nologies (pp. 271-282). Cham: Springer Nature Switzerland.
Haghpanah, M. A., Vali, S., Torkamani, A. M., Masouleh, M. T., Kalhor, A., & Sarraf, E. A. (2023). Real-time hand rub-bing quality estimation using deep learning enhanced by separation index and feature-based confidence metric. Expert Systems with Applications, 119588.
Hemsley, B., Balandin, S., Palmer, S., & Dann, S. (2017). A call for innovative social media research in the field of aug-mentative and alternative communication. Augmentative and Alternative Communication, 33(1), 14-22.
Hitelman, A., Edan, Y., Godo, A., Berenstein, R., Lepar, J., & Halachmi, I. (2022). Biometric identification of sheep via a machine-vision system. Computers and Electronics in Agriculture, 194, 106713.
Hramov, A. E., Maksimenko, V. A., & Pisarchik, A. N. (2021). Physical principles of brain–computer interfaces and their applications for rehabilitation, robotics and control of human brain states. Physics Reports, 918, 1-133.
Hsieh, Y. H., Granlund, M., Odom, S. L., Hwang, A. W., & Hemmingsson, H. (2022). Increasing participation in computer activities using eye-gaze assistive technology for children with complex needs. Disability and Rehabilitation: Assistive Technology, 1-14.
Jafri, R., Ali, S. A., & Arabnia, H. R. (2013). Computer vision-based object recognition for the visually impaired using vis-ual tags. In Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV) (p. 1). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).
Jiang, D., Li, G., Tan, C., Huang, L., Sun, Y., & Kong, J. (2021). Semantic segmentation for multiscale target based on ob-ject recognition using the improved Faster-RCNN model. Future Generation Computer Systems, 123, 94-104.
Ki, C. W. C., Cho, E., & Lee, J. E. (2020). Can an intelligent personal assistant (IPA) be your friend? Para-friendship devel-opment mechanism between IPAs and their users. Computers in Human Behavior, 111, 106412.
Krahn, G. L. (2011). WHO World Report on Disability: a review. Disability and health journal, 4(3), 141-142.
Krishnan, S., Mandala, M., Wolf, S. L., Howard, A., & Kesar, T. (2023). Perceptions of stroke survivors regarding factors affecting adoption of technology and exergames for rehabilitation. PM&R.
Lamontagne, M. E., Routhier, F., & Auger, C. (2013). Team consensus concerning important outcomes for augmentative and alternative communication assistive technologies: A pilot study. Augmentative and Alternative Communica-tion, 29(2), 182-189.
Layton, N., MacLachlan, M., Smith, R. O., & Scherer, M. (2020). Towards coherence across global initiatives in assistive technology. Disability and Rehabilitation: Assistive Technology, 15(7), 728-730.
Lee, S. H., & Yang, C. S. (2017). A real time object recognition and counting system for smart industrial camera sen-sor. IEEE Sensors Journal, 17(8), 2516-2523.
Lenker, J. A., Harris, F., Taugher, M., & Smith, R. O. (2013). Consumer perspectives on assistive technology out-comes. Disability and Rehabilitation: Assistive Technology, 8(5), 373-380.
Louhab, F. E., Bahnasse, A., Bensalah, F., Khiat, A., Khiat, Y., & Talea, M. (2020). Novel approach for adaptive flipped classroom based on learning management system. Education and Information Technologies, 25, 755-773.
Majil, I., Yang, M. T., & Yang, S. (2022). Augmented Reality Based Interactive Cooking Guide. Sensors, 22(21), 8290.
Medina, A., Méndez, J. I., Ponce, P., Peffer, T., Meier, A., & Molina, A. (2022). Using deep learning in real-time for clothing classification with connected thermostats. Energies, 15(5), 1811.
Mhasawade, V., Zhao, Y., & Chunara, R. (2021). Machine learning and algorithmic fairness in public and population health. Nature Machine Intelligence, 3(8), 659-666.
Milakis, D. (2019). Long-term implications of automated vehicles: An introduction. Transport Reviews, 39(1), 1-8.
Patil, V., Narayan, J., Sandhu, K., & Dwivedy, S. K. (2022). Integration of virtual reality and augmented reality in physical rehabilitation: a state-of-the-art review. Revolutions in Product Design for Healthcare: Advances in Product Design and Design Methods for Healthcare, 177-205.
Priestley, M. (2007). In search of European disability policy: Between national and global. Alter, 1(1), 61-74.
Ren, Z., Fang, F., Yan, N., & Wu, Y. (2022). State of the art in defect detection based on machine vision. International Jour-nal of Precision Engineering and Manufacturing-Green Technology, 9(2), 661-691.
Sahoo, S. K., & Choudhury, B. B. (2021). A Fuzzy AHP Approach to Evaluate the Strategic Design Criteria of a Smart Ro-botic Powered Wheelchair Prototype. In Intelligent Systems: Proceedings of ICMIB 2020 (pp. 451-464). Singapore: Springer Singapore.
Sahoo, S., & Choudhury, B. (2022). Optimal selection of an electric power wheelchair using an integrated COPRAS and EDAS approach based on Entropy weighting technique. Decision Science Letters, 11(1), 21-34.
Sahoo, S., & Choudhury, B. (2023). Voice-activated wheelchair: An affordable solution for individuals with physical disa-bilities. Management Science Letters, 13(3), 175-192.
Sahoo, S., & Goswami, S. (2024). Theoretical framework for assessing the economic and environmental impact of water pollution: A detailed study on sustainable development of India. Journal of Future Sustainability, 4(1), 23-34.
Shamsolmoali, P., Zareapoor, M., Granger, E., Zhou, H., Wang, R., Celebi, M. E., & Yang, J. (2021). Image synthesis with adversarial networks: A comprehensive survey and case studies. Information Fusion, 72, 126-146.
Sharadhi, A. K., Gururaj, V., Shankar, S. P., Supriya, M. S., & Chogule, N. S. (2022). Face mask recogniser using image processing and computer vision approach. Global Transitions Proceedings, 3(1), 67-73.
Silva Jr, E. T., Sampaio, F., da Silva, L. C., Medeiros, D. S., & Correia, G. P. (2020). A method for embedding a computer vision application into a wearable device. Microprocessors and Microsystems, 76, 103086.
Smith, R. O. (2016). The emergence and emergency of assistive technology outcomes research methodology. Assistive Tech-nology Outcomes & Benefits, 10(1), 19-37.
Szeto, A. (2005). Rehabilitation engineering and assistive technology. In Introduction to biomedical engineering (pp. 211-254). Academic Press.
Tavasoli, S., Pan, X., & Yang, T. Y. (2023). Real-time autonomous indoor navigation and vision-based damage assessment of reinforced concrete structures using low-cost nano aerial vehicles. Journal of Building Engineering, 68, 106193.
Vélez-Guerrero, M. A., Callejas-Cuervo, M., & Mazzoleni, S. (2021). Artificial intelligence-based wearable robotic exoskele-tons for upper limb rehabilitation: A review. Sensors, 21(6), 2146.
Ward, T. M., Mascagni, P., Ban, Y., Rosman, G., Padoy, N., Meireles, O., & Hashimoto, D. A. (2021). Computer vision in surgery. Surgery, 169(5), 1253-1256.
World Health Organization. (2021). WHO Policy on disability.
Yenugula, M., Sahoo, S., & Goswami, S. (2023). Cloud computing in supply chain management: Exploring the relation-ship. Management Science Letters, 13(3), 193-210.
Yenugula, M., Sahoo, S., & Goswami, S. (2024). Cloud computing for sustainable development: An analysis of environ-mental, economic and social benefits. Journal of future sustainability, 4(1), 59-66.
Yirtici, T., & Yurtkan, K. (2022). Regional-CNN-based enhanced Turkish sign language recognition. Signal, Image and Vid-eo Processing, 1-7.
Ymous, A., Spiel, K., Keyes, O., Williams, R. M., Good, J., Hornecker, E., & Bennett, C. L. (2020, April). " I am just terri-fied of my future"—Epistemic Violence in Disability Related Technology Research. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-16).
Zhou, T., Wang, W., Liang, Z., & Shen, J. (2021). Face forensics in the wild. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 5778-5788).
Akilan, T., Wu, Q. J., & Zhang, H. (2018). Effect of fusing features from multiple DCNN architectures in image classifica-tion. IET Image Processing, 12(7), 1102-1110.
Amtmann, D., McMullen, K., Bamer, A., Fauerbach, J. A., Gibran, N. S., Herndon, D, Schneider, J.C, Koalske, K.,Holavanahalli, R., & Miller, A. C. (2020). National institute on disability, independent living, and rehabilitation re-search burn model system: review of program and database. Archives of physical medicine and rehabilitation, 101(1), S5-S15.
Andrich, R. (2013). Service Delivery Systems for Assistive Technology in Europe: A Position Paper. In Assistive Technolo-gy: From Research to Practice (pp. 247-253). IOS Press.
Antoniadi, A. M., Du, Y., Guendouz, Y., Wei, L., Mazo, C., Becker, B. A., & Mooney, C. (2021). Current challenges and fu-ture opportunities for XAI in machine learning-based clinical decision support systems: a systematic review. Applied Sciences, 11(11), 5088.
Arigo, D., Lobo, A. F., Ainsworth, M. C., Baga, K., & Pasko, K. (2022). Development and initial testing of a personalized, adaptive, and socially focused web tool to support physical activity among women in midlife: multidisciplinary and us-er-centered design approach. JMIR Formative Research, 6(7), e36280.
Busaeed, S., Mehmood, R., Katib, I., & Corchado, J. M. (2022). LidSonic for Visually Impaired: Green Machine Learning-Based Assistive Smart Glasses with Smart App and Arduino. Electronics, 11(7), 1076.
Calabrò, R. S., Cerasa, A., Ciancarelli, I., Pignolo, L., Tonin, P., Iosa, M., & Morone, G. (2022). The Arrival of the Metaverse in Neurorehabilitation: Fact, Fake or Vision?. Biomedicines, 10(10), 2602.
Chai, J., Zeng, H., Li, A., & Ngai, E. W. (2021). Deep learning in computer vision: A critical review of emerging techniques and application scenarios. Machine Learning with Applications, 6, 100134.
Chen, C., Wang, Y., Niu, J., Liu, X., Li, Q., & Gong, X. (2021). Domain knowledge powered deep learning for breast cancer diagnosis based on contrast-enhanced ultrasound videos. IEEE Transactions on Medical Imaging, 40(9), 2439-2451.
de Belen, R. A. J., Bednarz, T., Sowmya, A., & Del Favero, D. (2020). Computer vision in autism spectrum disorder re-search: a systematic review of published studies from 2009 to 2019. Translational psychiatry, 10(1), 333.
Faria Oliveira, O. D., Carvalho Gonçalves, M., de Bettio, R. W., & Pimenta Freire, A. (2022). A qualitative study on the needs of visually impaired users in Brazil for smart home interactive technologies. Behaviour & Information Technology, 1-29.
Fazelpour, S., & Danks, D. (2021). Algorithmic bias: Senses, sources, solutions. Philosophy Compass, 16(8), e12760.
Finco, M. D., Dantas, V. R., & dos Santos, V. A. (2023). Exergames, Artificial Intelligence and Augmented Reality: Connec-tions to Body and Sensorial Experiences. In Augmented Reality and Artificial Intelligence: The Fusion of Advanced Tech-nologies (pp. 271-282). Cham: Springer Nature Switzerland.
Haghpanah, M. A., Vali, S., Torkamani, A. M., Masouleh, M. T., Kalhor, A., & Sarraf, E. A. (2023). Real-time hand rub-bing quality estimation using deep learning enhanced by separation index and feature-based confidence metric. Expert Systems with Applications, 119588.
Hemsley, B., Balandin, S., Palmer, S., & Dann, S. (2017). A call for innovative social media research in the field of aug-mentative and alternative communication. Augmentative and Alternative Communication, 33(1), 14-22.
Hitelman, A., Edan, Y., Godo, A., Berenstein, R., Lepar, J., & Halachmi, I. (2022). Biometric identification of sheep via a machine-vision system. Computers and Electronics in Agriculture, 194, 106713.
Hramov, A. E., Maksimenko, V. A., & Pisarchik, A. N. (2021). Physical principles of brain–computer interfaces and their applications for rehabilitation, robotics and control of human brain states. Physics Reports, 918, 1-133.
Hsieh, Y. H., Granlund, M., Odom, S. L., Hwang, A. W., & Hemmingsson, H. (2022). Increasing participation in computer activities using eye-gaze assistive technology for children with complex needs. Disability and Rehabilitation: Assistive Technology, 1-14.
Jafri, R., Ali, S. A., & Arabnia, H. R. (2013). Computer vision-based object recognition for the visually impaired using vis-ual tags. In Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV) (p. 1). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).
Jiang, D., Li, G., Tan, C., Huang, L., Sun, Y., & Kong, J. (2021). Semantic segmentation for multiscale target based on ob-ject recognition using the improved Faster-RCNN model. Future Generation Computer Systems, 123, 94-104.
Ki, C. W. C., Cho, E., & Lee, J. E. (2020). Can an intelligent personal assistant (IPA) be your friend? Para-friendship devel-opment mechanism between IPAs and their users. Computers in Human Behavior, 111, 106412.
Krahn, G. L. (2011). WHO World Report on Disability: a review. Disability and health journal, 4(3), 141-142.
Krishnan, S., Mandala, M., Wolf, S. L., Howard, A., & Kesar, T. (2023). Perceptions of stroke survivors regarding factors affecting adoption of technology and exergames for rehabilitation. PM&R.
Lamontagne, M. E., Routhier, F., & Auger, C. (2013). Team consensus concerning important outcomes for augmentative and alternative communication assistive technologies: A pilot study. Augmentative and Alternative Communica-tion, 29(2), 182-189.
Layton, N., MacLachlan, M., Smith, R. O., & Scherer, M. (2020). Towards coherence across global initiatives in assistive technology. Disability and Rehabilitation: Assistive Technology, 15(7), 728-730.
Lee, S. H., & Yang, C. S. (2017). A real time object recognition and counting system for smart industrial camera sen-sor. IEEE Sensors Journal, 17(8), 2516-2523.
Lenker, J. A., Harris, F., Taugher, M., & Smith, R. O. (2013). Consumer perspectives on assistive technology out-comes. Disability and Rehabilitation: Assistive Technology, 8(5), 373-380.
Louhab, F. E., Bahnasse, A., Bensalah, F., Khiat, A., Khiat, Y., & Talea, M. (2020). Novel approach for adaptive flipped classroom based on learning management system. Education and Information Technologies, 25, 755-773.
Majil, I., Yang, M. T., & Yang, S. (2022). Augmented Reality Based Interactive Cooking Guide. Sensors, 22(21), 8290.
Medina, A., Méndez, J. I., Ponce, P., Peffer, T., Meier, A., & Molina, A. (2022). Using deep learning in real-time for clothing classification with connected thermostats. Energies, 15(5), 1811.
Mhasawade, V., Zhao, Y., & Chunara, R. (2021). Machine learning and algorithmic fairness in public and population health. Nature Machine Intelligence, 3(8), 659-666.
Milakis, D. (2019). Long-term implications of automated vehicles: An introduction. Transport Reviews, 39(1), 1-8.
Patil, V., Narayan, J., Sandhu, K., & Dwivedy, S. K. (2022). Integration of virtual reality and augmented reality in physical rehabilitation: a state-of-the-art review. Revolutions in Product Design for Healthcare: Advances in Product Design and Design Methods for Healthcare, 177-205.
Priestley, M. (2007). In search of European disability policy: Between national and global. Alter, 1(1), 61-74.
Ren, Z., Fang, F., Yan, N., & Wu, Y. (2022). State of the art in defect detection based on machine vision. International Jour-nal of Precision Engineering and Manufacturing-Green Technology, 9(2), 661-691.
Sahoo, S. K., & Choudhury, B. B. (2021). A Fuzzy AHP Approach to Evaluate the Strategic Design Criteria of a Smart Ro-botic Powered Wheelchair Prototype. In Intelligent Systems: Proceedings of ICMIB 2020 (pp. 451-464). Singapore: Springer Singapore.
Sahoo, S., & Choudhury, B. (2022). Optimal selection of an electric power wheelchair using an integrated COPRAS and EDAS approach based on Entropy weighting technique. Decision Science Letters, 11(1), 21-34.
Sahoo, S., & Choudhury, B. (2023). Voice-activated wheelchair: An affordable solution for individuals with physical disa-bilities. Management Science Letters, 13(3), 175-192.
Sahoo, S., & Goswami, S. (2024). Theoretical framework for assessing the economic and environmental impact of water pollution: A detailed study on sustainable development of India. Journal of Future Sustainability, 4(1), 23-34.
Shamsolmoali, P., Zareapoor, M., Granger, E., Zhou, H., Wang, R., Celebi, M. E., & Yang, J. (2021). Image synthesis with adversarial networks: A comprehensive survey and case studies. Information Fusion, 72, 126-146.
Sharadhi, A. K., Gururaj, V., Shankar, S. P., Supriya, M. S., & Chogule, N. S. (2022). Face mask recogniser using image processing and computer vision approach. Global Transitions Proceedings, 3(1), 67-73.
Silva Jr, E. T., Sampaio, F., da Silva, L. C., Medeiros, D. S., & Correia, G. P. (2020). A method for embedding a computer vision application into a wearable device. Microprocessors and Microsystems, 76, 103086.
Smith, R. O. (2016). The emergence and emergency of assistive technology outcomes research methodology. Assistive Tech-nology Outcomes & Benefits, 10(1), 19-37.
Szeto, A. (2005). Rehabilitation engineering and assistive technology. In Introduction to biomedical engineering (pp. 211-254). Academic Press.
Tavasoli, S., Pan, X., & Yang, T. Y. (2023). Real-time autonomous indoor navigation and vision-based damage assessment of reinforced concrete structures using low-cost nano aerial vehicles. Journal of Building Engineering, 68, 106193.
Vélez-Guerrero, M. A., Callejas-Cuervo, M., & Mazzoleni, S. (2021). Artificial intelligence-based wearable robotic exoskele-tons for upper limb rehabilitation: A review. Sensors, 21(6), 2146.
Ward, T. M., Mascagni, P., Ban, Y., Rosman, G., Padoy, N., Meireles, O., & Hashimoto, D. A. (2021). Computer vision in surgery. Surgery, 169(5), 1253-1256.
World Health Organization. (2021). WHO Policy on disability.
Yenugula, M., Sahoo, S., & Goswami, S. (2023). Cloud computing in supply chain management: Exploring the relation-ship. Management Science Letters, 13(3), 193-210.
Yenugula, M., Sahoo, S., & Goswami, S. (2024). Cloud computing for sustainable development: An analysis of environ-mental, economic and social benefits. Journal of future sustainability, 4(1), 59-66.
Yirtici, T., & Yurtkan, K. (2022). Regional-CNN-based enhanced Turkish sign language recognition. Signal, Image and Vid-eo Processing, 1-7.
Ymous, A., Spiel, K., Keyes, O., Williams, R. M., Good, J., Hornecker, E., & Bennett, C. L. (2020, April). " I am just terri-fied of my future"—Epistemic Violence in Disability Related Technology Research. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-16).
Zhou, T., Wang, W., Liang, Z., & Shen, J. (2021). Face forensics in the wild. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 5778-5788).