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NavCog: a navigational cognitive assistant for the blind

Published:06 September 2016Publication History

ABSTRACT

Turn-by-turn navigation is a useful paradigm for assisting people with visual impairments during mobility as it reduces the cognitive load of having to simultaneously sense, localize and plan. To realize such a system, it is necessary to be able to automatically localize the user with sufficient accuracy, provide timely and efficient instructions and have the ability to easily deploy the system to new spaces.

We propose a smartphone-based system that provides turn-by-turn navigation assistance based on accurate real-time localization over large spaces. In addition to basic navigation capabilities, our system also informs the user about nearby points-of-interest (POI) and accessibility issues (e.g., stairs ahead). After deploying the system on a university campus across several indoor and outdoor areas, we evaluated it with six blind subjects and showed that our system is capable of guiding visually impaired users in complex and unfamiliar environments.

References

  1. Dragan Ahmetovic, Cristian Bernareggi, Andrea Gerino, and Sergio Mascetti. 2014. Zebrarecognizer: Efficient and precise localization of pedestrian crossings. In 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2566--2571. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Dragan Ahmetovic, Cole Gleason, Kris M. Kitani, Hironobu Takagi, and Chieko Asakawa. 2016. NavCog: turn-by-turn smartphone navigation assistant for people with visual impairments or blindness. In Proceedings of the 13th Web for All Conference. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Tomohiro Amemiya, Jun Yamashita, Koichi Hirota, and Michitaka Hirose. 2004. Virtual leading blocks for the deaf-blind: A real-time way-finder by verbal-nonverbal hybrid interface and high-density RFID tag space. In Virtual Reality, 2004. Proceedings. IEEE. IEEE, 165--287. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Masatoshi Arikawa, Shin'ichi Konomi, and Keisuke Ohnishi. 2007. NAVITIME: Supporting pedestrian navigation in the real world. IEEE Pervasive Computing 3, 6 (2007), 21--29. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Paramvir Bahl and Venkata N. Padmanabhan. 2000. RADAR: An in-building RF-based user location and tracking system. In INFOCOM 2000. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, Vol. 2. Ieee, 775--784.Google ScholarGoogle Scholar
  6. J. M. Barlow, B. L. Bentzen, D. Sauerburger, and L. Franck. 2010. Teaching travel at complex intersections. Foundations of orientation and mobility 2 (2010), 352--419.Google ScholarGoogle Scholar
  7. Blindsquare. 2016. Blindsquare. http://blindsquare.com/. (2016). {Online; accessed 10-Feb-2016}.Google ScholarGoogle Scholar
  8. Erin L. Brady, Daisuke Sato, Chengxiong Ruan, Hironobu Takagi, and Chieko Asakawa. 2015. Exploring Interface Design for Independent Navigation by People with Visual Impairments. In Proceedings of the 17th International ACM SIGACCESS Conference on Computers & Accessibility. ACM, 387--388. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Sakmongkon Chumkamon, Peranitti Tuvaphanthaphiphat, and Phongsak Keeratiwintakorn. 2008. A blind navigation system using RFID for indoor environments. In Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2008. ECTI-CON 2008. 5th International Conference on, Vol. 2. IEEE, 765--768.Google ScholarGoogle Scholar
  10. James Coughlan and Roberto Manduchi. 2009. Functional assessment of a camera phone-based wayfinding system operated by blind and visually impaired users. International Journal on Artificial Intelligence Tools 18, 03 (2009), 379--397.Google ScholarGoogle ScholarCross RefCross Ref
  11. Javier J. M. Diaz, Rodrigo de A. Maues, Rodrigo B. Soares, Eduardo F. Nakamura, and Carlos Figueiredo. 2010. Bluepass: An indoor bluetooth-based localization system for mobile applications. In Computers and Communications (ISCC), 2010 IEEE Symposium on. IEEE, 778--783. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Edsger W. Dijkstra. 1959. A note on two problems in connexion with graphs. Numerische mathematik 1, 1 (1959), 269--271. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Karen Duarte, José Cecílio, and Pedro Furtado. 2014. Easily Guiding of Blind: Providing Information and Navigation-SmartNav. In Wireless Internet. Springer, 129--134.Google ScholarGoogle Scholar
  14. Everywaretechnologies. 2016. iMove. http://www.everywaretechnologies.com/apps/imove. (2016). {Online; accessed 10-Feb-2016}.Google ScholarGoogle Scholar
  15. Navid Fallah, Ilias Apostolopoulos, Kostas Bekris, and Eelke Folmer. 2012. The user as a sensor: navigating users with visual impairments in indoor spaces using tactile landmarks. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 425--432. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Ramsey Faragher and R. Harle. 2014. An analysis of the accuracy of bluetooth low energy for indoor positioning applications. In Proceedings of the 27th International Technical Meeting of the Satellite Division of the Institute of Navigation.Google ScholarGoogle Scholar
  17. José Faria, Sérgio Lopes, Hugo Fernandes, Paulo Martins, and João Barroso. 2010. Electronic white cane for blind people navigation assistance. In World Automation Congress. IEEE.Google ScholarGoogle Scholar
  18. Brian Ferris, Dirk Haehnel, and Dieter Fox. 2006. Gaussian processes for signal strength-based location estimation. In In proc. of robotics science and systems. Citeseer.Google ScholarGoogle ScholarCross RefCross Ref
  19. Alexander Fiannaca, Ilias Apostolopoulous, and Eelke Folmer. 2014. Headlock: A wearable navigation aid that helps blind cane users traverse large open spaces. In Proceedings of the 16th international ACM SIGACCESS conference on Computers & accessibility. ACM, 19--26. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Thomas Gallagher, Elyse Wise, Binghao Li, Andrew G. Dempster, Chris Rizos, and Euan Ramsey-Stewart. 2012. Indoor positioning system based on sensor fusion for the Blind and Visually Impaired. In Indoor Positioning and Indoor Navigation. IEEE.Google ScholarGoogle Scholar
  21. Giuseppe Ghiani, Barbara Leporini, and Fabio Paternò. 2009. Vibrotactile feedback to aid blind users of mobile guides. Journal of Visual Languages & Computing 20, 5 (2009), 305--317. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Sendero Group. 2016. The Seeing Eye. http://www.seeingeye.org/. (2016). {Online; accessed 10-Feb-2016}.Google ScholarGoogle Scholar
  23. Hideyuki Iwahashi. 1983. Toward white wave - Story of Seiichi Miyake (in Japanese). Traffic Safety Research Center.Google ScholarGoogle Scholar
  24. Sergio Mascetti, Dragan Ahmetovic, Andrea Gerino, and Cristian Bernareggi. 2016. ZebraRecognizer: Pedestrian Crossing Recognition for People with Visual Impairment or Blindness. Pattern Recognition (2016).Google ScholarGoogle Scholar
  25. Sergio Mascetti, Dragan Ahmetovic, Andrea Gerino, Cristian Bernareggi, Mario Busso, and Alessandro Rizzi. 2015. Robust traffic lights detection on mobile devices for pedestrians with visual impairment. Computer Vision and Image Understanding (2015). Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Sergio Mascetti, Lorenzo Picinali, Andrea Gerino, Dragan Ahmetovic, and Cristian Bernareggi. 2016. Sonification of guidance data during road crossing for people with visual impairments or blindness. International Journal of Human-Computer Studies 85 (2016), 16--26. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. M. May and K. Casey. 2012. Accessible Global Positioning Systems (GPS). CRC Press.Google ScholarGoogle Scholar
  28. Marko Modsching, Ronny Kramer, and Klaus ten Hagen. 2006. Field trial on GPS Accuracy in a medium size city: The influence of built-up. In 3rd workshop on positioning, navigation and communication. 209--218.Google ScholarGoogle Scholar
  29. Marius Muja and David G. Lowe. 2009. Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration. VISAPP (1) 2 (2009), 331--340.Google ScholarGoogle Scholar
  30. Madoka Nakajima and Shinichiro Haruyama. 2012. Indoor navigation system for visually impaired people using visible light communication and compensated geomagnetic sensing. In Communications in China. IEEE.Google ScholarGoogle Scholar
  31. Ling Pei, Ruizhi Chen, Jingbin Liu, Heidi Kuusniemi, Tomi Tenhunen, and Yuwei Chen. 2010. Using inquiry-based Bluetooth RSSI probability distributions for indoor positioning. Journal of Global Positioning Systems 9, 2 (2010), 122--130.Google ScholarGoogle Scholar
  32. Helen Petrie, Valerie Johnson, Thomas Strothotte, Andreas Raab, Steffi Fritz, and Rainer Michel. 1996. MoBIC: Designing a travel aid for blind and elderly people. Journal of navigation 49, 01 (1996), 45--52.Google ScholarGoogle ScholarCross RefCross Ref
  33. Lisa Ran, Sumi Helal, and Steve Moore. 2004. Drishti: an integrated indoor/outdoor blind navigation system and service. In Conference on Pervasive Computing and Communications. IEEE. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Günther Retscher, Michael Thienelt, and others. 2004. NAVIO--A navigation and guidance service for pedestrians. Positioning 1, 08 (2004).Google ScholarGoogle Scholar
  35. Fazli Subhan, Halabi Hasbullah, Azat Rozyyev, and Sheikh Tahir Bakhsh. 2011. Indoor positioning in bluetooth networks using fingerprinting and lateration approach. In Information Science and Applications (ICISA), 2011 International Conference on. IEEE, 1--9.Google ScholarGoogle ScholarCross RefCross Ref
  36. Michele A. Williams, Caroline Galbraith, Shaun K. Kane, and Amy Hurst. 2014. just let the cane hit it: how the blind and sighted see navigation differently. In Proceedings of the 16th international ACM SIGACCESS conference on Computers & accessibility. ACM, 217--224. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Xiaojie Zhao, Zhuoling Xiao, Andrew Markham, Niki Trigoni, and Yong Ren. 2014. Does BTLE measure up against WiFi? A comparison of indoor location performance. In European Wireless Conference. VDE.Google ScholarGoogle Scholar

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            cover image ACM Conferences
            MobileHCI '16: Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services
            September 2016
            567 pages
            ISBN:9781450344081
            DOI:10.1145/2935334

            Copyright © 2016 ACM

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            Publication History

            • Published: 6 September 2016

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