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Obstacle and Fall Detection to Guide the Visually Impaired People with Real Time Monitoring

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Abstract

To assist the visually impaired people to travel independently without external aid and monitoring real-time location information of these individuals, a wearable electronic device is presented in this paper. The system is able to detect the obstacles in front of the user, humps on the ground, moving objects. In addition, the system detects the sudden fall and informs the user’s guardian. The system is comprised of ultrasonic sensors, a PIR motion sensor, an accelerometer, a smartphone application, a microcontroller, and a data transmission device. The microcontroller transmits the data to the user’s smartphone via Bluetooth module. The smartphone application generates audible instructions to navigate the user properly. The application also updates the current location of the user to keep track and notifies the guardians when the user falls down or in distress. The developed system obtained an accuracy of about 98.34% when the obstacle is 50 cm away from the user. The system is proved to be very effective and efficient for users to navigate using precise speech instructions. Overall, the developed system will make the visually impaired people and their guardian’s feel much safer and confident.

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References

  1. Blindness and vision impairment [Online]. https://www.who.int/news-room/fact-sheets/detail/blindness-and-visual-impairment. Accessed 3 Apr 2019

  2. Lakde CK, Prakash SP. Review paper on navigation system for visually impaired people. Int J Adv Res Comput Commun Eng. 2015;4(1):166–8.

    Article  Google Scholar 

  3. Bhatlawande S, Mahadevappa M, Mukherjee J, Biswas M, Das D, Gupta S. Design, development, and clinical evaluation of the electronic mobility cane for vision rehabilitation. IEEE Trans Neural Syst Rehabil Eng. 2014;22(6):1148–59.

    Article  Google Scholar 

  4. Jafri R, Campos RL, Ali SA, Arabnia HR. Visual and infrared sensor data-based obstacle detection for the visually impaired using the Google project tango tablet development kit and the unity engine. IEEE Access. 2018;6:443–54.

    Article  Google Scholar 

  5. Islam MM, Sheikh Sadi M, Zamli KM, Ahmed MM. Developing walking assistants for visually impaired people: a review. IEEE Sens J. 2019;19(8):2814–28.

    Article  Google Scholar 

  6. Alam MN, Islam MM, Habib MA, Mredul MB. Staircase detection systems for the visually impaired people: a review. Int J Comput Sci Inf Security (IJCSIS). Dec. 2018;16(12):13–8.

    Google Scholar 

  7. Islam MM, Sadi MS (2018) Path hole detection to assist the visually impaired people in navigation. In: Proceedings of 4th International conference on electrical engineering and information and communication technology (iCEEiCT), Dhaka, Bangladesh, pp 268–273.

  8. Kamal MM, Bayazid AI, Sadi MS, Islam MM, Hasan N (2017) Towards developing walking assistants for the visually impaired people. In: Proceedings IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, pp. 238–241.

  9. Rezwanul Haque M, Islam MM, Saeed Alam K, Iqbal H. A computer vision based lane detection approach. Int J Image Graph Signal Process. 2019;11(3):27–34.

    Article  Google Scholar 

  10. Islam MM, Sadi MS, Islam MM, Hasan MK (2018) A new method for road surface detection. 2018 4th International conference on electrical engineering and information & communication technology (iCEEiCT), Dhaka, Bangladesh, pp. 624–629

  11. Islam MM, Islam MR, Islam MS. An efficient human computer interaction through hand gesture using deep convolutional neural network. SN Comput Sci. Jun. 2020;1(4):223.

    Article  Google Scholar 

  12. Haque S, Sadi MS, Rafi MEH, Islam MM, Hasan MK (2020) Real-time crowd detection to prevent stampede. In: Uddin M, Bansal J, editors Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems, pp. 665–678. https://doi.org/10.1007/978-981-13-7564-4_56

  13. Hasan M, Islam MM, Zarif MII, Hashem MMA. Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet Things. Sep. 2019;7:100059.

    Article  Google Scholar 

  14. Mahmud S, Lin X, Kim J (2020) Interface for human machine interaction for assistant devices: a review. 10th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, pp. 0768-0773

  15. Mahmud S et al (2019) A multi-modal human machine interface for controlling a smart wheelchair. 2019 IEEE 7th conference on systems, process and control (ICSPC), Melaka, Malaysia, pp. 10–13.

  16. Shaha A, Rewari S, Gunasekharan S (2018) SWSVIP-smart walking stick for the visually impaired people using low latency communication. 2018 International conference on smart city and emerging technology (ICSCET), Mumbai, pp. 1–5

  17. Shahira KC, Tripathy S, Lijiya A (2019) Obstacle detection, depth estimation and warning system for visually impaired people. TENCON 2019—2019 IEEE Region 10 Conference (TENCON), Kochi, India, pp. 863–68

  18. Agrawal MP, Gupta AR. Smart Stick for the Blind and Visually Impaired People. In: 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, pp. 542–45, 2018.

  19. O’Brien EE, Mohtar AA, Diment LE, Reynolds KJ. A detachable electronic device for use with a long white cane to assist with mobility. Assist Technol. 2014;26(4):219–26.

    Article  Google Scholar 

  20. Sahoo N, Lin H-W, Chang Y-H. Design and implementation of a walking stick aid for visually challenged people. Sensors. 2019;19:130. https://doi.org/10.3390/s19010130.

    Article  Google Scholar 

  21. Chen LB, Su JP, Chen MC, Chang WJ, Yang CH, Sie CY. An Implementation of an Intelligent Assistance System for Visually Impaired/Blind People. 2019. https://doi.org/10.1109/ICCE.2019.8661943.

  22. Meshram VV, Patil K, Meshram VA, Shu FC. An astute assistive device for mobility and object recognition for visually impaired people. IEEE Trans Hum Mach Syst. Oct. 2019;49(5):449–60.

    Article  Google Scholar 

  23. Islam MM, Rahaman A, Islam MR. Development of smart healthcare monitoring system in IoT environment. SN Comput Sci. May 2020;1(3):185.

    Article  Google Scholar 

  24. Rahaman A, Islam MM, Islam MR, Sadi MS, Nooruddin S. Developing IoT based smart health monitoring system: a review. Revue d'Intelligence Artificielle, Lavoisier. 2019;33(6):435–40.

    Article  Google Scholar 

  25. ESP32 vs ESP8266 – Pros and Cons, [Online]. Available:https://makeradvisor.com/esp32-vs-esp8266/. Accessed 9 Dec 2018

  26. Rahman MA, Sadi MS, Islam MM, Saha P. Design and Development of Navigation Guide for Visually Impaired People. In: 2019 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON), Dhaka, Bangladesh, 2019, pp. 89–92

  27. Habib MA, Islam MM, Kabir MN, Mredul MB, Hasan M. Staircase detection system for visually impaired people: a hybrid approach. Revue d'Intelligence Artificielle Lavoisier. 2019;33(5):327–34.

    Article  Google Scholar 

  28. Islam MM, Neom NH, Imtiaz MS, Nooruddin S, Islam MR, Islam MR. A review on fall detection systems using data from smartphone sensors. Ingénierie des Systèmes d’Information, Lavoisier. 2019;24(6):569–76.

    Article  Google Scholar 

  29. Nooruddin S, Islam MM, Sharna FA. An IoT based device-type invariant fall detection system. Internet Things. 2020;9:100130.

    Article  Google Scholar 

  30. PIR Sensor – Basics & Applications, [Online]. Available: https://www.elprocus.com/pir-sensor-basics-applications/. Accessed 9 Dec 2018.

  31. Rahman MM, Islam MM, Ahmmed S. BlindShoe: an electronic guidance system for the visually impaired people. J Telecommun Electron Comput Eng (JTEC). 2019;11(2):49–4.

    Google Scholar 

  32. Sadi MS, Mahmud S, Kamal MM, Bayazid AI. Automated walk-in assistant for the blinds. In: 2014 International Conference on Electrical Engineering and Information & Communication Technology, Dhaka, 2014, pp. 1–4.

  33. Zhou D, Yang Y, Yan H. A smart \"virtual eye\" mobile system for the visually impaired. IEEE Potentials. 2016;35(6):13–20.

    Article  Google Scholar 

  34. Sharma S, Gupta M, Kumar A, Tripathi M, Gaur MS. Multiple distance sensors based smart stick for visually impaired people. In: 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, 2017, pp. 1–5.

  35. Khanom M, Sadi MS, Islam MM. A Comparative Study of Walking Assistance Tools Developed for the Visually Impaired People. In: 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), Dhaka, Bangladesh, 2019, pp. 1–5.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh

    Mohammad Marufur Rahman, Md. Milon Islam, Shishir Ahmmed & Saeed Anwar Khan

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  1. Mohammad Marufur Rahman
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  2. Md. Milon Islam
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  3. Shishir Ahmmed
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Correspondence to Md. Milon Islam.

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This article is part of the topical collection “Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications” guest edited by Bhanu Prakash K N and M. Shivakumar.

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Rahman, M.M., Islam, M.M., Ahmmed, S. et al. Obstacle and Fall Detection to Guide the Visually Impaired People with Real Time Monitoring. SN COMPUT. SCI. 1, 219 (2020). https://doi.org/10.1007/s42979-020-00231-x

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