Journal Of Applied and Computational Sciences in Mechanics

Journal Of Applied and Computational Sciences in Mechanics

Assistive system to guide visually impaired people behind work desks

Document Type : Original Article

Authors
Amirmohammad Barsalani 1
Hamidreza Mohammadi Daniali 1
Arman Mardani 2
1 Department of Mechanical Engineering, Babol Noshirvani University of Technology, Mazandaran
2 Department of Mechanical Engineering, Babol Noshirvani University of Technology, Mazandaran, Iran.
10.22067/jacsm.2025.91402.1310
Abstract
According to the World Health Organization (WHO), over 38 million people are blind and more than 110 million suffer from severe visual impairments. This research aims to develop a low-cost, accessible assistance system for blind individuals in work environments, utilizing a single camera (e.g., a smartphone camera) and pre-trained deep learning models. By employing computationally efficient image processing techniques, the system optimizes hardware and processing expenses. It detects objects and the user’s hand, computes object heights and their distances to the hand, and then provides real-time audio guidance (e.g., verbal directions) to enable safe object retrieval. Compared to expensive alternatives like dual-camera setups or Kinect sensors, the core innovation lies in dramatically reducing costs while retaining functional accuracy, as millimeter-level precision is unnecessary for workplace navigation. System performance will be validated to achieve a distance estimation error of approximately 5-10 mm; its accuracy is anticipated to match that of comparable stereo vision approaches.
Keywords
Low-cost
Blind Individuals
Workstation Assistance
Object Detection
Audio Guidance
Pre-trained Models
Subjects
World Helath Organization, “Rehabilitation,” [Online]. [Accessed: Apr. 22, 2024]. Available: https://www.who.int/news-room/fact-sheets/detail/rehabilitation 
[1]
J. D. Edwards, E. G. Valdés, C. Peronto, M. Castora-Binkley, J. Alwerdt, R. Andel, J. J. Lister, “The efficacy of InSight cognitive training to improve useful field of view performance: A brief report,” Journals of Gerontology Series B: Psychological Sciences and Social Sciences, vol. 70, no. 3, pp. 417–422, 2015. https://doi.org/10.1093/geronb/gbt113
[2]
A. Colenbrander, “My Perspective on Vision and Vision Rehabilitation,” Ophthalmology Science, p. 100532, 2024. https://doi.org/10.1016/j.xops.2024.100532
[3]
M. E. Wilkinson, K. S. Shahid, “Low vision rehabilitation: An update,” Saudi Journal of Ophthalmology, vol. 32, no. 2, pp. 134–138, 2018. https://doi.org/10.1016/j.sjopt.2017.10.005
[4]
I. Kardan, A. Akbarzadeh, “A New Hybrid Strategy for Forward Kinematic Analysis of Parallel Robots (Research Note),” The Journal of Applied and Computational Sciences in Mechanics, pp. 163–172, 2016. http://dx.doi.org/10.1080/01691864.2014.994034
[5]
M. Nizamuddin, A. Kamisetty, J. C. S Gummadi, and R. R Talla, "Integrating Neural Networks with Robotics: Towards Smarter Autonomous Systems and Human-Robot Interaction," Robotics Xplore: USA Tech Digest 1, no. 1, pp. 157-169, 2024.
[6]
A. Sadallah, J. Riazat, “Parameter Optimization of Various Solar Cell Models by Neural Network Algorithm,” Journal of Applied & Computational Sciences in Mechanics, vol. 36, no. 1, pp. 123-142, 2024. https://doi.org/10.22067/jacsm.2023.81702.1176  
[7]
V. Modanloo, A. Mashayekhi, and B. Akhoundi, “Comparison of regression, bee colony, and artificial neural network for predicting die filling in stamping of bipolar plates fuel cell,” Journal of Applied and Computational Sciences in Mechanics, vol. 36, no. 2, pp. 47–58, 2024. https://doi.org/10.22067/jacsm.2023.84567.1205
[8]
S. A. Bagherzadeh, S. Emami Koopaei, and A. Salehi, “Optimal design of divergent part of an over-expanded nozzle profile using artificial intelligence,” Journal of Applied and Computational Sciences in Mechanics, vol. 35, no. 1, pp. 19–36, 2023. https://doi.org/10.22067/jacsm.2022.78011.1134
[9]
M. Taghi Zadeh, M. Mokhtari, and M. Mazare, “Fixed-time fault tolerant control based on neural network and adaptive sliding mode controller of autonomous underwater vehicle with actuator saturation,” Journal of Applied and Computational Sciences in Mechanics, vol. 36, no. 4, pp. 35–56, 2024. https://doi.org/10.22067/jacsm.2024.85527.1219
[10]
A. Liu, H. Zhao, T. Song, Z. Liu, H. Wang, and D. Sun, "Adaptive control of manipulator based on neural network," Neural Computing and Applications, vol 33, no. 9, pp. 4077-4085, 2021. https://doi.org/10.1007/s00521-020-05515-0
[11]
S. Targ, D. Almeida, K. Lyman, “Resnet in resnet: Generalizing residual architectures,” arXiv preprint arXiv:1603.08029, 2016. https://doi.org/10.48550/arXiv.1603.08029
[12]
K. Simonyan, A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014. https://doi.org/10.48550/arXiv.1409.1556
[13]
P. Jiang, D. Ergu, F. Liu, Y. Cai, and Bo Ma, "A Review of Yolo algorithm developments," Procedia Computer Science, vol. 199, pp. 1066-1073, 2022. https://doi.org/10.1016/j.procs.2022.01.135
[14]
T. L. Narayani, M. Sivapalanirajan, B. Keerthika, M. Ananthi, and M. Arunarani, “Design of smart cane with integrated camera module for visually impaired people,” in Proceeding 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), 2021, pp. 999–1004. https://doi.org/10.1109/ICAIS50930.2021.9395840
[15]
M. Marcon, A. Ortoncelli, “Object Recognition to Support Navigation Systems for Blind in Uncontrolled Environments,” Anais do Computer on the Beach, vol. 13, pp. 274–281, 2022. https://doi.org/10.14210/cotb.v13.p274-281
[16]
J. Kilian, A. Neugebauer, L. Scherffig, S. Wahl, “The unfolding space glove: A wearable spatio-visual to haptic sensory substitution device for blind people,” Sensors, vol. 22, no. 5, p. 1859, 2022. https://doi.org/10.3390/s22051859
[17]
M. Aharchi, M. H. A. Kbir, “Localization and Navigation System for Blind Persons Using Stereo Vision and a GIS,” in WITS 2020: Proceedings of the 6th International Conference on Wireless Technologies, Embedded, and Intelligent Systems, Springer, 2022, pp. 1–12. https://doi.org/10.1007/978-981-33-6893-4_35
[18]
T. Adep, R. Nikam, S. Wanewe, and D. K. B. Naik, “Visual assistant for blind people using Raspberry Pi,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 3307, pp. 671–675, 2021. http://dx.doi.org/10.32628/CSEIT2173142
[19]
P. Zantou, A. M. Mousse, B. Atohoun, “An Intelligent based System for Blind People Monitoring in a Smart Home.” Computer Science & Information Technology (CS & IT), 2020. https://doi.org/10.5121/csit.2020.101910
[20]
K. T. Banu, K. Supriya, K. Sony, M. Chandana, M. Bhavana, M. B. F. Ali, “Outdoor & Indoor Vision Based Localization for Blind Pedestrian Navigation Assistance,” International Journal of Engineering Applied Sciences and Technology, vol. 4, no. 12, pp. 715-720, 2020. http://dx.doi.org/10.33564/IJEAST.2020.v04i12.127
[21]
O. Younis, W. Al-Nuaimy, F. Rowe, M. H. Alomari, “A smart context-aware hazard attention system to help people with peripheral vision loss,” Sensors, vol. 19, no. 7, p. 1630, 2019. https://doi.org/10.3390/s19071630
[22]
H. P. Buimer, M. Bittner, T. Kostelijk, T. M. Van Der Geest, A. Nemri, R. J. Van Wezel, Y. Zhao, “Conveying facial expressions to blind and visually impaired persons through a wearable vibrotactile device,” PloS One, vol. 13, no. 3, p. 0194737, 2018. https://doi.org/10.1371/journal.pone.0194737
[23]
Y. Bouteraa, “Smart real time wearable navigation support system for BVIP,” Alexandria Engineering Journal, vol. 62, pp. 223–235, 2023. https://doi.org/10.1016/j.aej.2022.06.060
[24]
S. Tennekoon, N. Abhayasinghe, and N. Wedasingha. "Object Recognition and Assistance System for Visually Impaired Shoppers," Proceedings of the SLIIT International Conference On Engineering and Technology, vol. 2, 2023. https://doi.org/10.54389/RRFZ2311
[25]
E. J. Argüello Prada, and L. M. Santacruz Forero, "A belt-like assistive device for visually impaired people: Toward a more collaborative approach," Cogent Engineering, vol. 9, no. 1, p. 2048440, 2022. https://doi.org/10.1080/23311916.2022.2048440
[26]
A.V. Canez, J. Sartori, R. Barwaldt, and R. N. Rodrigues, "Collision detection with monocular vision for assisting in mobility of visually impaired people." In 2019 8th Brazilian Conference on Intelligent Systems (BRACIS), 2019, pp. 269-274. http://dx.doi.org/10.1109/BRACIS.2019.00055
[27]
H. P. Buimer, M. Bittner, T. Kostelijk, T. M. Van Der Geest, A. Nemri, R. J. A. Van Wezel, and Y. Zhao. "Conveying facial expressions to blind and visually impaired persons through a wearable vibrotactile device." PloS one 13, no. 3 , p. 0194737, 2018. https://doi.org/10.1371/journal.pone.0194737
[28]
Y. Lin, K. Wang, W. Yi, and S. Lian, "Deep learning based wearable assistive system for visually impaired people," In Proceedings of the IEEE/CVF international conference on computer vision workshops, 2019. https://doi.org/10.48550/arXiv.1908.03364
[29]
B. Kuriakose, I. M. Ness, M. Å. skov Tengstedt, J. M. Svendsen, T. Bjørseth, B. L. Pradhan, and R. Shrestha, "Turn Left Turn Right-Delving type and modality of instructions in navigation assistant systems for people with visual impairments," International Journal of Human-Computer Studies, vol. 179, p. 103098, 2023. https://doi.org/10.1016/j.ijhcs.2023.103098
[30]
M. A. Hussain, M. G. Ullah, A. Fareed, and B. Sohail, "The smartcane for blind people an electronically smart stick to aid mobility," International Journal of Computer Science and Information Security, vol. 14, no. 4, p. 276, 2016.
[31]
S. Mattoccia and P. Macrì, “3D glasses as mobility aid for visually impaired people,” in Computer Vision – ECCV 2014 Workshops, Zurich, Switzerland, Sept. 6–7 and 12, 2014, Proceedings, Part III, vol. 13, pp. 643–658, 2015. https://doi.org/10.1007/978-3-319-16199-0_38
[32]
M. Sohan, T. Sai Ram, C.V. Rami Reddy, “A review on YOLOv8 and its advancements,” in International Conference on Data Intelligence and Cognitive Informatics, Springer, 2024, pp. 1–8. https://doi.org/10.1007/978-981-99-7962-2_39
[33]
Ultralytics, “YOLOv8 Documentation,” [Online]. Available: https://docs.ultralytics.com/models/yolov8, [Accessed: Nov.12, 2023].
[34]
F. Zhang, V. Bazarevsky, A. Vakunov, A. Tkachenka, G. Sung, C. L. Chang, and M. Grundmann, "Mediapipe hands: On-device real-time hand tracking," arXiv preprint arXiv:2006.10214, 2020. https://doi.org/10.48550/arXiv.2006.10214
[35]
R. A. Setyawan, R. Soenoko, P. Mudjirahardjo, and M. A Choiron, "Measurement accuracy analysis of distance between cameras in stereo vision," In 2018 Electrical power, electronics, communications, controls and informatics seminar (EECCIS), 2018, pp. 169-172. https://doi.org/10.1109/EECCIS.2018.8692999
[36]
Send comment about this article
Enter Name.
Enter a valid email address.
Enter a vaid affiliation.
Enter comments (At leaset 10 words)
CAPTCHA Image
Enter Security Code Correctly.