Abstract
With the development of technology, many inventions have been made which have helped to make the lives of differently abled people easier. Prosthetic arms and legs have been developed, hearing aids are now readily available, glasses and contact lenses are available for people who have myopia or hypermetropia, motor-operated wheelchairs are available for people with impaired legs. Most of the benefits of technology advancement have little consideration for the visually impaired even though they constitute about 3.6% of world’s population. However, with the advent of artificial intelligence, machine learning and the Internet of things, different types of helping aids have been developed to facilitate a visually challenged person to navigate. Unfortunately, these helping aids either have limited scopes and too many constraints or are very expensive. The device intends to assist a visually impaired person to walk around by integrating machine learning algorithms and image processing techniques.
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References
Wolffe KE, Roessler RT, Schriner KF (1992) Employment concerns of people with blindness or visual impairments. J Vis Impair Blind 86:185–187
Murphy E, Kuber R, McAllister G, Strain P, Yu W (2008) An empirical investigation into the difficulties experienced by visually impaired Internet users. J Univ Access Inf Soc 7:79–91
Xiao J, Joseph SL, Zhang X, Li B, Li X, Zhang J (2015) An assistive navigation framework for the visually impaired. IEEE Trans Human-Mach Syst 635–640
Hersh M, Johnson MA (2008) Assistive technology for visually impaired and blind people
Islam MM, Sadi MS, Zamli KZ, Ahmed MM (2019) Developing walking assistants for visually impaired people: a review. IEEE Sens J 19(8):2814–2828
Hu M, Chen Y, Zhai G, Gao Z, Fan L (2019) An overview of assistive devices for blind and visually impaired people. J Robo Auto 34:580–598
Tapu R, Mocanu B, Zaharia T (2020) Wearable assistive devices for visually impaired: a state of the art survey. Pattern Recogn Lett 137:37–52
Yuvaraju EC, Rudresh LR, Saimurugan M (2020) Vibration signals based fault severity estimation of a shaft using machine learning techniques. Mat Today Proc 24:241–250
Saimurugan M, Praveenkumar T, Krishnakumar P, Ramachandran KI (2015) A study on the classification ability of decision tree and support vector machine in gearbox fault detection. J.A.M.M vol 813. Trans Tech Publications Ltd., pp 1058–1062
Saimurugan M, Ramachandran KI (2014) A comparative study of sound and vibration signals in detection of rotating machine faults using support vector machine and independent component analysis. Int J Data Anal Tech Strat 6(2):188–204
Mohanraj T, Yerchuru J, Krishnan H, Aravind RN, Yameni R (2021) Development of tool condition monitoring system in end milling process using wavelet features and Hoelder’s exponent with machine learning algorithms. Measurement 173:108671
Sreenath PG, Praveen Kumare G, Pravin S, Vikram KN, Saimurugan M (2015) Automobile gearbox fault diagnosis using Naive Bayes and decision tree algorithm. In: Applied mechanics and materials, vol 813. Trans Tech Publications Ltd., pp 943–948
Praveenkumar T, Saimurugan M, Krishnakumar P, Ramachandran KI (2014) Fault diagnosis of automobile gearbox based on machine learning techniques. Proc Eng 97:2092–2098
Terven JR, Salas J, Raducanu B (2013) New opportunities for computer vision-based assistive technology systems for the visually impaired. J Comp 47:52–58
Bhowmick A, Hazarika SM (2017) An insight into assistive technology for the visually impaired and blind people: state-of-the-art and future trends. J Multimodel User Interfaces 11:149–172
Nalini A (2018) Smart walking assistance device with embedded control system. IJPAM. 120(6):137–145
Al-Muqbali F, Al-Tourshi N, Al-Kiyumi K, Hajmohideen F (2020) Smart technologies for visually impaired: assisting and conquering infirmity of blind people using AI technologies. In: 2020 12th annual undergraduate research conference on applied computing (URC). IEEE, pp 1–4
Arakeri MP, Keerthana NS, Madhura M, Sankar A, Munnavar T (2018)Assistive technology for the visually impaired using computer vision. In: 2018 international conference on advances in computing, ICACCI. IEEE, pp 1725–1730
Kumar S, Mathew S, Anumula N, Chandra KS (2019) Portable camera-based assistive device for real-time text recognition on various products and speech using android for blind people. In: Innovations in electronics and communication engineering, pp 437–448
Mann WC, Hurren D, Karuza J, Bentley DW (1993) Needs of home-based older visually impaired persons for assistive devices. J Visual Impairment Blind 87(4):106–110
Elmannai W, Elleithy K (2017) Sensor-based assistive devices for visually-impaired people: current status, challenges, and future directions. J Sensors 17(3):565
Hakobyan L, Lumsden J, O’Sullivan D, Bartlett H (2013) Mobile assistive technologies for the visually impaired. J Sur Ophth 58(6):513–528
Min S, Oh Y (2020) Survey on obstacle detection features of smart technologies to help visually impaired people walk. J Korea Ind Info Sys Res 25(3):31–38
Massof RW Auditory assistive devices for the blind. Georgia Institute of Technology
Scheggi S, Talarico A, Prattichizzo D (2014) A remote guidance system for blind and visually impaired people via vibrotactile haptic feedback. In: 22nd mediterranean conference on control and automation. IEEE, pp 20–23
Palanisamy K, Arunkumar K, Bhuvaneshwaran P, Naveenkumar S, Dhamodharan M (2017) Walking stick with OPCFD system. Global Res Devel J Eng 3(1):1–5
Dhanuja R, Farhana F, Savitha G (2018) Smart blind stick using Arduino. J IRJET 5(03)
Aymaz Ş, Çavdar T (2016) Ultrasonic assistive headset for visually impaired people. In: 39th international conference on telecommunications and signal processing. IEEE, pp 388–391
Herghelegiu P, Burlacu A, Caraiman S (2017) Negative obstacle detection for wearable assistive devices for visually impaired. In: 2017 21st international conference on system theory, control and computing (ICSTCC). IEEE, Sinaia, pp 564–570
Kamal MM, Bayazid AI, Sadi MS, Islam MM, Hasan N (2017) Towards developing walking assistants for the visually impaired people. In: 2017 IEEE region 10 humanitarian technology conference (R10-HTC). IEEE, pp 238–241
Velázquez R (2010) Wearable assistive devices for the blind. In: Lay-Ekuakille A, Mukhopadhyay SC (eds) Wearable and autonomous biomedical devices and systems for smart environment. Lecture notes in electrical engineering, vol 75. Springer, Heidelberg.
Priya T, Sravya KS, Umamaheswari S (2020) Machine-learning-based device for visually impaired person. Artificial intelligence and evolutionary computations in engineering systems, pp 79–88
Dahlin, Ivanoff S, Sonn U (2004) Use of assistive devices in daily activities among 85-year olds living at home focusing especially on the visually impaired. J Disability Rehab 26(24):1423–1430
Panchanathan S, Black J, Rush M, Iyer V (2003) iCare-a user centric approach to the development of assistive devices for the blind and visually impaired. In: 15th IEEE international conference on tools with artificial intelligence. IEEE, pp 641–648
Krishna S, Colbry D, Black J, Balasubramanian V, Panchanathan S (2008) A systematic requirements analysis and development of an assistive device to enhance the social interaction of people who are blind or visually impaired. In: Workshop on computer vision applications for the visually impaired, James Coughlan and Roberto Manduchi, Marseille, France
Lan W, Dang J, Wang Y, Wang S (2018) Pedestrian detection based on YOLO network model. In: IEEE international conference on mechatronics and automation (ICMA). IEEE, pp 1547–1551
Shafiee MJ, Chywl B, Li F, Wong A (2017) Fast YOLO: a fast you only look once system for real-time embedded object detection in video. arXiv:1709.05943
Huang R, Pedoeem J, Chen C (2018) YOLO-LITE: a real-time object detection algorithm optimized for non-GPU computers. In: IEEE international conference on big data. IEEE, pp 2503–2510
Jeong HJ, Park KS, Ha YG (2018) Image preprocessing for efficient training of YOLO deep learning networks. In: IEEE international conference on big data and smart computing. IEEE, pp 635–637
Du J (2018) Understanding of object detection based on CNN family and YOLO. J Phys Conf Ser 1004(1):012029. IOP Publishing
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Lokare, A.S., Venkatesh, P., Sitthanathan, S.V., Mohanraj, T. (2023). Development of Walking Assistants for Visually Challenged Person. In: Asari, V.K., Singh, V., Rajasekaran, R., Patel, R.B. (eds) Computational Methods and Data Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol 139. Springer, Singapore. https://doi.org/10.1007/978-981-19-3015-7_3
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DOI: https://doi.org/10.1007/978-981-19-3015-7_3
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