Virtual Vision Architecture for VIP in Ubiquitous Computing

  • Soubraylu Sivakumar
  • Ratnavel Rajalakshmi
  • Kolla Bhanu Prakash
  • Baskaran Rajesh Kanna
  • Chinnasamy Karthikeyan
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)


Visually Impaired People (VIP) have to move in the highly dense society alone. In real world situation, they have to overcome more obstacles, hurdles and traffic while they navigate indoor and outdoor. Even more sophisticated technology cannot help those people for their convenience navigation and utility. The virtual vision architecture is composed of different subsystem. This architecture includes Head Obstacle Detection system, Tail Obstacle Detection system (TOD), Positioning and Location System, Alerting and Notification System, Information Management System (IMS) and Speech Recognizer Engine. IMS consists of Selenium web driver that is used to retrieve the latest information from various web servers. It is a newly proposed method to communicate with the existing web server. A TOD system is capable of monitoring the moving objects that comes behind the VIP. The proposed idea includes three methods for calculating the distance of the moving object. The speed is calculated from the distance. Based on the speed, the walking direction of the VIP is adjusted to avoid an accident.


Visually impaired people (VIP) Head obstacle detection system (HOD) Positioning and location system (PLS) Alerting and notification system (ANS) Information management system (IMS) Tail obstacle detection system (TOD) Speech recognizer engine (SRE) Selenium 


  1. AlAbri, H. A., AlWesti, A. M., AlMaawali, M. A., & AlShidhani, A. A. (2014). NavEye: Smart guide for blind students. In Systems and Information Engineering Design Symposium (SIEDS-IEEE), (pp. 141–146).
  2. Baig, M. M., & Gholamhosseini, H. (2013). Smart health monitoring systems: An overview of design and modeling. Journal of Medical Systems, 37, 9898. Scholar
  3. Carlson, R., & Granström, B. (1986). A search for durational rules in a real-speech data base. Phonetica, 43, 140–154. Scholar
  4. Chaudary, B., Paajala, I., Keino, E., & Pulli, P. (2016). Tele-guidance based navigation system for the visually impaired and blind persons. In K. Giokas, L. Bokor, & F. Hopfgartner (Eds.), eHealth 360° (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering) (Vol. 181, pp. 9–16). Cham: Springer. Scholar
  5. Chen, A. Q., & Goh, W. (2015). Two factor authentication made easy. In International Conference on Web Engineering (pp. 449–458). New York: Springer. Scholar
  6. Chung, I. Y., Kim, S., & Rhee, K. H. (2014). The smart cane utilizing a smart phone for the visually impaired person. In 3rd Global Conference on Consumer Electronics (GCCE-IEEE) (pp. 106–107).
  7. Eldefrawy, M. H., Alghathbar, K., & Khan, M. K. (2011). OTP-based two-factor authentication using mobile phones. In Eighth International Conference on Information Technology: New Generations (IEEE), (pp. 327–331). 2011.64
  8. Elson, J., Douceur, J. R., Howell, J., & Saul, J. (2007). Asirra: A CAPTCHA that exploits interest-aligned manual image categorization. In Proceedings of 14th ACM Conference on Computer and Communications Security (CCS). New York, NY: Association for Computing Machinery, Inc. Scholar
  9. Fukushima, K. (1980). Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36(4), 93–202. Scholar
  10. Gao, M., Hu, X., Cao, B., & Li, D. (2014). Fingerprint sensors in mobile devices. In 9th IEEE Conference on Industrial Electronics and Applications (IEEE) (pp. 1437–1440). 394
  11. Gazdar, G., & Mellish, C. S. (1990). Natural language processing in Lisp: An introduction to computational linguistics (Computational Linguistics) (Vol. 16(2)). Boston, MA: Addison-Wesley Longman Publishing Co.Google Scholar
  12. Ghidini, E., Almeida, W. D. L., Manssour, I. H., & Silveira, M. S. (2016). Developing apps for visually impaired people: Lessons learned from practice. In 49th Hawaii International Conference on System Sciences (IEEE) (pp. 5691–5700).
  13. Huang, X. D., Acero, A., & Hon, H. (2001). Spoken language processing—A guide to theory, algorithms, and system development. Upper Saddle River, NJ: Prentice Hall.Google Scholar
  14. Jackson, C. L., Konopka, J. B., & Hartwell, L. H. (1991). S. cerevisiae alpha pheromone receptors activate a novel signal transduction pathway for mating partner discrimination. Cell, 67(2), 389–402. Scholar
  15. Karthikeyan, C., & Ramadoss, B. (2014). Non linear fusion technique based on dual tree complex wavelet transform. International Journal of Applied Engineering Research, 9(22), 13375–13385. Scholar
  16. Karthikeyan, C., & Ramadoss, B. (2015). Fusion of medical images using mutual information and intensity based image registration schemes. ARPN Journal of Engineering and Applied Sciences, 10(8), 3561–3565. Scholar
  17. Karthikeyan, C., & Ramadoss, B. (2016). Comparative analysis of similarity measure performance for multimodality image fusion using DTCWT and SOFM with various medical image fusion techniques. Indian Journal of Science and Technology, 9(22).
  18. Karthikeyan, C., Ramadoss, B., & Baskar, S. (2011). Segmentation algorithm for CT images using morphological operation and artificial neural network. International Journal of Computer Theory and Engineering, 3(4), 561–564. Scholar
  19. Kasper, K., & Reininger, H. (1999). Evaluation of pemo in robust speech recognition. The Journal of the Acoustical Society of America, 104(2), 1175. Scholar
  20. Kato, M., & Hirata, K. (2017). Dynamic characteristics of linear resonant actuator using electrical resonance. In Conference on Electromagnetic Field Computation (CEFC-IEEE) (p. 1).
  21. Kingma, D. P., & Ba, J. L. (2015). Adam: A method for stochastic optimization. In International Conference on Learning Representations (pp. 1–13).
  22. Krause, J., Stark, M., Deng, J., & Fei-Fei, L. (2013). 3D object representations for fine-grained categorization. In 4th IEEE Workshop on 3D Representation and Recognition, at ICCV 2013 (3dRR-13), Sydney, Australia.
  23. Lee, J.-H., Kim, K., Lee, S.-C., & Shin, B.-S. (2013). Smart backpack for visually impaired person. In International Conference on ICT for Smart Society (IEEE) (pp. 1–4).
  24. Li, Z., Pundlik, S., & Luo, G. (2013). Stabilization of magnified videos on a mobile device for visually impaired. In IEEE Conference on Computer Vision and Pattern Recognition Workshops.
  25. Machida, E., Cao, M., Murao, T., & Hashimoto, H. (2012). Human motion tracking of mobile robot with Kinect 3D sensor. In Proceedings of the SICE Annual Conference (IEEE) (pp. 2207–2211).
  26. McTear, M. F. (2004). Components of a spoken dialogue system—Speech input and output. In Spoken dialogue technology (pp. 79–105). London: Springer. Scholar
  27. Prakash, K. B., & Dorai Rangaswamy, M. A. (2016a). Content extraction of biological datasets using soft computing techniques. Journal of Medical Imaging and Health Informatics, 6, 932–936. Scholar
  28. Prakash, K. B., & Dorai Rangaswamy, M. A. (2016b). Content extraction studies using neural network and attribute generation. Indian Journal of Science and Technology, 9(22), 1–10. Scholar
  29. Prakash, K. B., & RajaRaman, A. (2016). Mining of bilingual Indian Web documents. Procedia Computer Science, 89, 514–520. Scholar
  30. Prudtipongpun, V., Buakeaw, W., Rattanapongsen, T., & Sivaraksa, M. (2015). Indoor navigation system for vision-impaired individual: An application on android devices. In International Conference on Signal-Image Technology & Internet-Based Systems (SITIS-IEEE) (pp. 633–638.
  31. Rajalakshmi, R., & Agrawal, R. (2017). Borrowing likeliness ranking based on relevance factor. In Fourth ACM IKDD Conference on Data Science (ACM), (pp. 1–2).
  32. Rajalakshmi, R., & Aravindan, C. (2011). Naive Bayes approach for website classification. In International Conference on Advances in Information Technology and Mobile Communication (pp. 323–326). New York: Springer. Scholar
  33. Rajalakshmi, R., & Aravindan, C. (2013). Web page classification using n-gram based URL features. In Fifth International Conference on Advanced Computing (ICoAC-IEEE) (pp. 15–21).
  34. Ramani, S. V., & Tank, Y. N. (2014). Indoor navigation on Google Maps and indoor localization using RSS Fingerprinting. International Journal of Engineering Trends and Technology, 11(4), 171–173. Scholar
  35. Ramya, P., Sindhura, V., & Vidya Sagar, P. (2017). Testing using selenium web driver. In Second International Conference on Electrical, Computer and Communication Technologies (ICECCT-IEEE) (pp. 1–7).
  36. Rodríguez, J., Goñi, A., & Illarramendi, A. (2003). Capturing, analysing, and managing ECG sensor data in handheld devices. In OTM Confederated International Conferences On the Move to Meaningful Internet Systems (pp. 1133–1150). Cham: Springer. Scholar
  37. Sivakumar, S., Arun, P., Lokeshvarma, R., & Krishnakumar, P. (2015). Android based traffic updates. International Journal of Scientific Research in Science, 1(1), 161–164.Google Scholar
  38. Sivakumar, S., Brindha, S., Deepalakshmi, D., Dhivya, T., Arul, U., & Nattar Kannan, K. (2017). ISCAP: Intelligent and smart cryptosystem in android phone. In International Conference on Power and Embedded Drive Control (ICPEDC-IEEE), (pp. 453–458). 8081132
  39. Sivakumar, S., Jennifer, J., Marrison, M. N., Seetha, J., & Sathish Saravanan, P. (2017). DMMRA: Dynamic medical machine for rural areas. In International Conference On Power And Embedded Drive Control (ICPEDC-IEEE) (pp. 467–471). 35
  40. Sivakumar, S., Kamatchi, K., Sangeetha, R., Subha, V., & Ramachandran, R. (2017). D2CMUS: Detritus to Cinder conversion and managing through ultrasonic sensor. In Third International Conference on Science Technology Engineering & Management (ICONSTEM-IEEE) (pp. 38–43).
  41. Sivakumar, S., Kasthuri, R., Nivetha, B., Shabana, S., & Veluchamy, M. (2017). Smart device for visually impaired people. In Third International Conference on Science Technology Engineering & Management (ICONSTEM-IEEE) (pp. 54–59).
  42. Sivakumar, S., & Rajalakshmi, R. (2017). Comparative evaluation of various feature weighting methods on movie reviews. In International Conference on Computational Intelligence in Data Mining. New York, NY: Springer. Scholar
  43. Sivakumar, S., Saranu, P. N., Abirami, G., RameshKumar, M., Arul, U., & Seetha, J. (2018). Theft detection system using PIR sensor. In 4th International Conference on Electrical Energy Systems (IEEE), (pp. 321–324).
  44. Sivakumar, S., Thirumalai Raj, R., & Sanjay, S. (2016). Digital license mv. In International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET-IEEE) (pp. 1277–1280).
  45. Tahir, A. S. (2015). Design and implementation of RSA algorithm using FPGA. International Journal of Computers & Technology, 14(12), 6361–6367. Scholar
  46. Van Den Bosch, A., & Daelemans, W. (1993). Data-oriented methods for grapheme-to-phoneme conversion. In ACL Anthology-A Digital Archive of Research Papers in Computational Linguistics (pp. 1–9).
  47. Van Noord, G., Bouma, G., Koeling, R., & Nederhof, M. J. (1999). Robust grammatical analysis for spoken dialogue systems. Natural Language Engineering, 5(1), 45–93. Scholar
  48. Varadan, V. K., & Varadan, V. V. (2000). Wireless MEMS-IDT based accelerometer and gyroscope in a single chip. Smart Materials Bulletin, 2000(12), 9–13. Scholar
  49. Zang, F., & Zhang, J.-S. (2011). Softmax discriminant classifier. In Third International Conference on Multimedia Information Networking and Security (IEEE) (pp. 16–19).
  50. Zhou, D., Yang, Y., & Yan, H. (2016). A smart “virtual eye” mobile system for the visually impaired. IEEE Potentials, 35, 13–20. Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Soubraylu Sivakumar
    • 1
  • Ratnavel Rajalakshmi
    • 2
  • Kolla Bhanu Prakash
    • 1
  • Baskaran Rajesh Kanna
    • 2
  • Chinnasamy Karthikeyan
    • 1
  1. 1.Computer Science EngineeringKoneru Lakshmaiah Education FoundationGunturIndia
  2. 2.Computing Science EngineeringVellore Institute of TechnologyChennaiIndia

Personalised recommendations