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Achieving Practical and Accurate Indoor Navigation for People with Visual Impairments

Published: 02 April 2017 Publication History

Abstract

Methods that provide accurate navigation assistance to people with visual impairments often rely on instrumenting the environment with specialized hardware infrastructure. In particular, approaches that use sensor networks of Bluetooth Low Energy (BLE) beacons have been shown to achieve precise localization and accurate guidance while the structural modifications to the environment are kept at minimum. To install navigation infrastructure, however, a number of complex and time-critical activities must be performed. The BLE beacons need to be positioned correctly and samples of Bluetooth signal need to be collected across the whole environment. These tasks are performed by trained personnel and entail costs proportional to the size of the environment that needs to be instrumented.
To reduce the instrumentation costs while maintaining a high accuracy, we improve over a traditional regression-based localization approach by introducing a novel, graph-based localization method using Pedestrian Dead Reckoning (PDR) and particle filter. We then study how the number and density of beacons and Bluetooth samples impact the balance between localization accuracy and set-up cost of the navigation environment. Studies with users show the impact that the increased accuracy has on the usability of our navigation application for the visually impaired.

References

[1]
D. Ahmetovic, C. Bernareggi, A. Gerino, and S. Mascetti. Zebrarecognizer: Efficient and precise localization of pedestrian crossings. In 2014 22nd International Conference on Pattern Recognition (ICPR), pages 2566--2571. IEEE, 2014.
[2]
D. Ahmetovic, C. Gleason, K. Kitani, H. Takagi, and C. Asakawa. Navcog: turn-by-turn smartphone navigation assistant for people with visual impairments or blindness. In Web for All Conference. ACM, 2016.
[3]
D. Ahmetovic, C. Gleason, C. Ruan, K. Kitani, H. Takagi, and C. Asakawa. Navcog: A navigational cognitive assistant for the blind. In Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services, MobileHCI '16. ACM, 2016.
[4]
D. Ahmetovic, R. Manduchi, J. Coughlan, and S. Mascetti. Mind your crossings: Mining gis imagery for crosswalk localization. ACM Transactions on Accessible Computing, 2016.
[5]
D. Ahmetovic, R. Manduchi, J. M. Coughlan, and S. Mascetti. Zebra crossing spotter: automatic population of spatial databases for increased safety of blind travelers. In Proceedings of the 17th International ACM SIGACCESS Conference on Computers & Accessibility, pages 251--258. ACM, 2015.
[6]
T. Amemiya, J. Yamashita, K. Hirota, and M. Hirose. 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, pages 165--287. IEEE, 2004.
[7]
J. Benjamin and J. Malvern. The new c-5 laser cane for the blind. In Proc. Carnahan Conf. on Electronic Prosthetics, pages 77--82, 1973.
[8]
B. Blasch, S. LaGrow, and W. De l'Aune. Three aspects of coverage provided by the long cane: Object, surface, and foot-placement preview. Journal of Visual Impairment and Blindness, 90:295--301, 1996.
[9]
A. Brajdic and R. Harle. Walk detection and step counting on unconstrained smartphones. In Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing, pages 225--234. ACM, 2013.
[10]
M. Brock and P. O. Kristensson. Supporting blind navigation using depth sensing and sonification. In Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication, pages 255--258. ACM, 2013.
[11]
S. Chumkamon, P. Tuvaphanthaphiphat, and P. Keeratiwintakorn. 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, volume 2, pages 765--768. IEEE, 2008.
[12]
J. M. Coughlan and A. L. Yuille. The manhattan world assumption: Regularities in scene statistics which enable bayesian inference. In NIPS, volume 2, page 3, 2000.
[13]
S. L. Dockstader and A. M. Tekalp. Multiple camera tracking of interacting and occluded human motion. Proceedings of the IEEE, 89(10):1441--1455, 2001.
[14]
N. Fallah, I. Apostolopoulos, K. Bekris, and E. Folmer. 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, pages 425--432. ACM, 2012.
[15]
R. Faragher and R. Harle. Location fingerprinting with bluetooth low energy beacons. Selected Areas in Communications, IEEE Journal on, 33(11):2418--2428, 2015.
[16]
J. Faria, S. Lopes, H. Fernandes, P. Martins, and J. Barroso. Electronic white cane for blind people navigation assistance. In World Automation Congress. IEEE, 2010.
[17]
A. Fiannaca, I. Apostolopoulous, and E. Folmer. 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, pages 19--26. ACM, 2014.
[18]
A. Fod, A. Howard, and M. Mataric. A laser-based people tracker. In Robotics and Automation, 2002. Proceedings. ICRA'02. IEEE International Conference on, volume 3, pages 3024--3029. IEEE, 2002.
[19]
J. Gonzalez, J. Blanco, C. Galindo, A. Ortiz-de Galisteo, J. Fernández-Madrigal, F. Moreno, and J. Martinez. Combination of uwb and gps for indoor-outdoor vehicle localization. In Intelligent Signal Processing, 2007. WISP 2007. IEEE International Symposium on. IEEE, 2007.
[20]
S. Hilsenbeck, D. Bobkov, G. Schroth, R. Huitl, and E. Steinbach. Graph-based data fusion of pedometer and wifi measurements for mobile indoor positioning. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pages 147--158. ACM, 2014.
[21]
H. Iwahashi. Toward white wave - Story of Seiichi Miyake (in Japanese). Traffic Safety Research Center, 1983.
[22]
H. Kacorri, S. Mascetti, A. Gerino, D. Ahmetovic, H. Takagi, and C. Asakawa. Supporting orientation of people with visual impairment: Analysis of large scale usage data. In International ACM SIGACCESS Conference on Computers and Accessibility. ACM, 2016.
[23]
G. Kleege. Visible braille/invisible blindness. Journal of visual culture, 5(2):209--218, 2006.
[24]
G. E. Legge, P. J. Beckmann, B. S. Tjan, G. Havey, K. Kramer, D. Rolkosky, R. Gage, M. Chen, S. Puchakayala, and A. Rangarajan. Indoor navigation by people with visual impairment using a digital sign system. PloS one, 8(10):e76783, 2013.
[25]
R. M. Leonard. Statistics on vision impairment: A resource manual.
[26]
R. Manduchi, J. Coughlan, K. Miesenberger, J. M. Coughlan, and H. Shen. Crosswatch: a system for providing guidance to visually impaired travelers at traffic intersection. Journal of Assistive Technologies, 7(2):131--142, 2013.
[27]
R. Manduchi and S. Kurniawan. Assistive technology for blindness and low vision. CRC Press, 2012.
[28]
S. Mascetti, D. Ahmetovic, A. Gerino, and C. Bernareggi. Zebrarecognizer: Pedestrian crossing recognition for people with visual impairment or blindness. Pattern Recognition, 2016.
[29]
S. Mascetti, D. Ahmetovic, A. Gerino, C. Bernareggi, M. Busso, and A. Rizzi. Robust traffic lights detection on mobile devices for pedestrians with visual impairment. Computer Vision and Image Understanding, 148:123--135, 2016.
[30]
M. Muja and D. G. Lowe. Fast approximate nearest neighbors with automatic algorithm configuration. VISAPP (1), 2:331--340, 2009.
[31]
K. P. Murphy. Machine learning: a probabilistic perspective. MIT press, 2012.
[32]
M. Nakajima and S. Haruyama. Indoor navigation system for visually impaired people using visible light communication and compensated geomagnetic sensing. In Communications in China. IEEE, 2012.
[33]
D. Peraković, M. Periša, and V. Remenar. Model of guidance for visually impaired persons in the traffic network. Transportation research part F: traffic psychology and behaviour, 31:1--11, 2015.
[34]
T. Poulsen. Acoustic traffic signal for blind pedestrians. Applied Acoustics, 15(5):363--376, 1982.
[35]
N. Pressey. Mowat sensor. Focus, 11(3):35--39, 1977.
[36]
J. Ryckaert, P. De Doncker, R. Meys, A. de Le Hoye, and S. Donnay. Channel model for wireless communication around human body. Electronics letters, 40(9):543--544, 2004.
[37]
S. Saito, A. Hiyama, T. Tanikawa, and M. Hirose. Indoor marker-based localization using coded seamless pattern for interior decoration. In 2007 IEEE Virtual Reality Conference. IEEE, 2007.
[38]
M. A. Williams, C. Galbraith, S. K. Kane, and A. Hurst. 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, pages 217--224. ACM, 2014.

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  • (2024)Perpetual Reconfigurable Intelligent Surfaces Through In-Band Energy Harvesting: Architectures, Protocols, and ChallengesIEEE Vehicular Technology Magazine10.1109/MVT.2023.334499419:1(36-44)Online publication date: Mar-2024
  • (2023)Identifying Indoor Objects Using Neutrosophic Reasoning for Mobility Assisting Visually Impaired PeopleApplied Sciences10.3390/app1304215013:4(2150)Online publication date: 7-Feb-2023
  • (2023)Exploring the User Experience of an AI-based Smartphone Navigation Assistant for People with Visual ImpairmentsProceedings of the 15th Biannual Conference of the Italian SIGCHI Chapter10.1145/3605390.3605421(1-8)Online publication date: 20-Sep-2023
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cover image ACM Other conferences
W4A '17: Proceedings of the 14th International Web for All Conference
April 2017
191 pages
ISBN:9781450349000
DOI:10.1145/3058555
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 02 April 2017

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Author Tags

  1. Indoor Localization
  2. Navigation Assistance
  3. Visual Impairments

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  • Research-article
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W4A '17
W4A '17: Web For All 2017 - The Future of Accessible Work
April 2 - 4, 2017
Western Australia, Perth, Australia

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W4A '17 Paper Acceptance Rate 22 of 33 submissions, 67%;
Overall Acceptance Rate 171 of 371 submissions, 46%

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Cited By

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  • (2024)Perpetual Reconfigurable Intelligent Surfaces Through In-Band Energy Harvesting: Architectures, Protocols, and ChallengesIEEE Vehicular Technology Magazine10.1109/MVT.2023.334499419:1(36-44)Online publication date: Mar-2024
  • (2023)Identifying Indoor Objects Using Neutrosophic Reasoning for Mobility Assisting Visually Impaired PeopleApplied Sciences10.3390/app1304215013:4(2150)Online publication date: 7-Feb-2023
  • (2023)Exploring the User Experience of an AI-based Smartphone Navigation Assistant for People with Visual ImpairmentsProceedings of the 15th Biannual Conference of the Italian SIGCHI Chapter10.1145/3605390.3605421(1-8)Online publication date: 20-Sep-2023
  • (2023)"I Want to Figure Things Out": Supporting Exploration in Navigation for People with Visual ImpairmentsProceedings of the ACM on Human-Computer Interaction10.1145/35794967:CSCW1(1-28)Online publication date: 16-Apr-2023
  • (2023)Scalable Indoor Wi-Fi Positioning Algorithm Using Cluster-Based Local Estimators2023 IEEE 8th International Conference on Recent Advances and Innovations in Engineering (ICRAIE)10.1109/ICRAIE59459.2023.10468356(1-6)Online publication date: 2-Dec-2023
  • (2023)An integrated region proposal and spatial information guided convolution network based object recognition for visually impaired persons’ indoor assistive navigationThe Imaging Science Journal10.1080/13682199.2023.2230419(1-14)Online publication date: 5-Jul-2023
  • (2023)Sonification of navigation instructions for people with visual impairmentInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2023.103057177(103057)Online publication date: Sep-2023
  • (2023)CityGuide: a seamless indoor–outdoor wayfinding system for people with vision impairmentsUniversal Access in the Information Society10.1007/s10209-023-01009-723:4(1843-1855)Online publication date: 28-Jun-2023
  • (2022)ASSISTER: Assistive Navigation via Conditional Instruction GenerationComputer Vision – ECCV 202210.1007/978-3-031-20059-5_16(271-289)Online publication date: 29-Oct-2022
  • (2022)Can Route Previews Amplify Building Orientation for People with Visual Impairment?Computers Helping People with Special Needs10.1007/978-3-031-08648-9_22(187-196)Online publication date: 1-Jul-2022
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