Design of Low Vision Electronic Glasses with Image Processing Capabilities Using Raspberry Pi

Rachmad Setiawan; Rayhan Akmal Fadlurahman; Nada Fitrieyatul Hikmah

doi:10.35882/jeeemi.v5i2.294

Rachmad Setiawan Department of Biomedical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia https://orcid.org/0000-0002-9724-0320
Rayhan Akmal Fadlurahman Department of Biomedical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
Nada Fitrieyatul Hikmah Department of Biomedical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia https://orcid.org/0000-0003-0182-1698

DOI: https://doi.org/10.35882/jeeemi.v5i2.294

Abstract

Poor vision is one of the most common eye health issues worldwide. Low vision patients are typically treated with optical devices or by substituting hearing or touch for visual capabilities. Head-mounted displays are currently the most promising form of low-vision assistive technology since they utilize the user's remaining natural visual capabilities. In this work, a prototype head-mounted display-based low-vision tool in the form of electronic glasses was designed utilizing a Raspberry Pi computer. The prototype was created using a Raspberry Pi 4 B coupled with cameras to allow real-time video acquisition. The LCD on the electronic eyewear frame as the camera showed the video recording. The prototype also included software utilizing five image processing modes—magnification, brightness enhancement, adaptive contrast enhancement, edge enhancement, and text detection and recognition- to help persons with limited vision acquire visual information more effectively. OpenCV was used with Python to create the software system. Average framerate measurements of 30–40 FPS for brightness and contrast improvement modes, 20 FPS for zooming and edge enhancement modes, and 1.3 FPS for text identification modes showed that the concept of electronic spectacles was successfully implemented in this research.

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References

S. J. Leat, G. E. Legge, and M. A. Bullimore, "What is low vision? A re-evaluation of definitions," Optom. Vis. Sci., vol. 76, no. 4, pp. 198–211, 1999, doi: 10.1097/00006324-199904000-00023.

GBD 2019 Blindness and Vision Impairment Collaborators, "Causes of blindness and vision impairment in 2020 and trends over 30 years , and prevalence of avoidable blindness in relation to VISION 2020 : the Right to Sight : an analysis for the Global Burden of Disease Study," Lancet Glob Health, vol. 9, no.2, pp. e144-e160, 2021, doi: 10.1016/S2214-109X(20)30489-7.

GBD 2019 Blindness and Vision Impairment Collaborators, "Trends in prevalence of blindness and distance and near vision impairment over 30 years : an analysis for the Global Burden of Disease Study," Lancet Glob Health, vol. 9, no.2, pp. e130-e143, 2021. doi: 10.1016/S2214-109X(20)30425-3.

A. Blackman, "An introduction to electronic low vision aids," Dispensing Optics, November, pp. 18–22, 2016. [Online]. Available: https://www.abdo.org.uk/wp-content/uploads/2016/11/DO-November-2016.pdf

J. H. Morris, L. Irvine, T. Tooman, S. U. Dombrowski, B. McCormack, F. V. Wijck, and M. Lawrence, "WeWalk: walking with a buddy after stroke-a pilot study evaluating feasibility and acceptability of a person-centred dyadic behaviour change intervention," Pilot Feasibility Stud., vol. 9, no. 1, 2023, doi: 10.1186/s40814-022-01227-5.

X. Nguyen, J. Koopman, M. M. Genderen, H. L. M. Stam, and C. J. F. Boon, "Artificial vision: the effectiveness of the OrCam in patients with advanced inherited retinal dystrophies," Acta Ophthalmologica, vol. 100, no. 4, 2022, doi: 10.1111/aos.15001.

A. D. Deemer et al., "Low Vision Enhancement with Head-mounted Video Display Systems: Are We There Yet?," Optom. Vis. Sci., vol. 95, no. 9, pp. 694–703, 2018, doi: 10.1097/OPX.0000000000001278.

B. I. Afinogenov et al., "Wearable optical-digital assistive device for low vision students," in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2016, pp. 2083–2086, doi: 10.1109/EMBC.2016.7591138.

H. Moshtael, T. Aslam, I. Underwood, and B. Dhillon, "High Tech Aids Low Vision : A Review of Image Processing for the Visually Impaired," Transl. Vis. Sci. Technol., vol. 4, no. 4, 2015, doi: 10.1167/tvst.4.4.6.

T. Brussee, T. J. T. P. Van Den Berg, R. M. A. Van Nispen, I. De Boer, and G. H. M. B. Van Rens, "Association between Contrast Sensitivity and Reading with Macular Pathology," Optom. Vis. Sci., vol. 95, no. 3, pp. 183–192, 2018, doi: 10.1097/OPX.0000000000001178.

P. T. Harvey, "Common eye diseases of elderly people: Identifying and treating causes of vision loss," Gerontology, vol. 49, no. 1, pp. 1–11, 2003, doi: 10.1159/000066507.

M. Kwon, C. Ramachandra, P. Satgunam, B. W. Mel, E. Peli, and B. S. Tjan, "Contour Enhancement Benefits Older Adults with Simulated Central Field Loss," Optom Vis. Sci., vol. 89, no. 9, pp. 1374–1384, 2012, doi: 10.1097/OPX.0b013e3182678e52.

Y. Zhao, S. Szpiro, L. Shi, and S. Azenkot, "Designing and evaluating a customizable head-mounted vision enhancement system for people with low vision," ACM Trans. Access. Comput., vol. 12, no. 4, 2019, doi: 10.1145/3361866.

J. R. Ehrlich et al., "Head-Mounted Display Technology for Low-Vision Rehabilitation and Vision Enhancement," Am. J. Ophthalmol., vol. 176, pp. 26–32, 2017, doi: 10.1016/j.ajo.2016.12.021.

M. Christen and M. Abegg, "The effect of magnification and contrast on reading performance in different types of simulated low vision," J. Eye Mov. Res., vol. 10, no. 2, 2017, doi: 10.16910/jemr.10.2.5.

A. R. Bowers, C. Meek, and N. Stewart, "Illumination and reading performance in age-related macular degeneration," Clin. Exp. Optom., vol. 84, no. 3, pp. 139–147, 2001, doi: 10.1111/j.1444-0938.2001.tb04957.x.

J. S. Wolffsohn, D. Mukhopadhyay, and M. Rubinstein, "Image Enhancement of Real-Time Television to Benefit the Visually Impaired," Am. J. Ophthalmol., vol. 144, no. 3, 2007, doi: 10.1016/j.ajo.2007.05.031.

R. Szeliski, Computer Vision: Algorithms and Applications, vol. 142. Washington, USA: Springer, 2011.

S. M. Pizer et al., "Adaptive histogram equalization and its variations," Computer Vision, Graphics, and Image Processing, vol. 38, no. 1. p. 99, 1987, doi: 10.1016/s0734-189x(87)80156-1.

A. Eshaghzadeh and R. S. Kalantari, "Canny Edge Detection Algorithm Application for Analysis of the Potential Field Map," Earth Sci. India, vol. 10, no. 3, 2017, doi: 10.31870/esi.10.3.2017.5.

R. Smith, "An Overview of the Tesseract OCR Engine BT - ICDAR 07: Proceedings of the Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) Vol 2," Lect. Google Code. Google Inc, pp. 629–633, 2007, [Online] Available: http://portal.acm.org/citation.cfm?id=1304846%5Cn/home/kermorvant/refbase_files/2007/smith/1152_smith2007.pdf.

R. Bansal, G. Raj, and T. Choudhury, "Blur image detection using Laplacian operator and Open-CV," Proc. 5th Int. Conf. Syst. Model. Adv. Res. Trends, SMART 2016, pp. 63–67, 2017, doi: 10.1109/SYSMART.2016.7894491.

L. A. Wasserman, All of Statistics: A Concise Course in Statistical Inference. 2004.

U. Sara, M. Akter, and M. S. Uddin, "Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study," J. Comput. Commun., vol. 07, no. 03, pp. 8–18, 2019, doi: 10.4236/jcc.2019.73002.

A. Rodriguez-molares, O. Marius, H. Rindal, D. Jan, A. Austeng, and H. Torp, “The Generalized Contrast-to-Noise Ratio: A Formal Definition for Lesion Detectability,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 67, no. 4, pp. 745-759, 2020.