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Overview of Smart White Canes: Connected Smart Cane from Front End to Back End

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Abstract

There are 285 million visually impaired people (VIP) worldwide, among whom 39 million are blind (WHO 2014).

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Notes

  1. 1.

    Cf. Part 3 of this book on mobility cognitive models.

  2. 2.

    http://www.ncbi.nlm.nih.gov/pubmed/18432492.

  3. 3.

    See also Part 4 of this book.

  4. 4.

    Although “laser” and “infrared” refer to different, independent aspects of light, and as such cannot perfectly discriminate sensors, we use the terms “infrared sensor” and “laser sensor” in their common sense: an infrared sensor uses an incoherent beam of infrared light, whereas a laser sensor uses a single coherent beam of light, visible or not. Furthermore, we call “laser sensor” only the sensors measuring the distance on a single point; we refer to higher dimensionality laser sensors (2D, 3D) as “LIDARs”.

  5. 5.

    Natural Language Toolkit: http://www.nltk.org/.

  6. 6.

    OpenTripPlanner: http://www.opentripplanner.org/.

  7. 7.

    Wheelmap: http://wheelmap.org.

References

  1. WHO (2014) Visual impairment and blindess. http://www.who.int/mediacentre/factsheets/fs282/en/. Accessed 13 April 2017

  2. Manduchi R, Kurniawan S (2011) Mobility-related accidents experienced by people with visual impairment. AER J Res Pract Vis Impairment and Blindness 4(2):44–54

    Google Scholar 

  3. Wiener W, Welsch R, Blasch B (eds) (2010) Foundation of orientation and mobility, volume I: history and theory. American Foundation for the Blind, New York

    Google Scholar 

  4. Thinus-Blanc C, Gaunet F (1997) Representation of space in blind persons: vision as a spatial sense? Psychol Bull 121:20–42. doi:10.1037/0033-2909.121.1.20

    Article  Google Scholar 

  5. Lévesque (2008) Blindness, Technology and Haptics. Survey, Haptics Laboratory, Centre for Intelligent Machines, McGill University, Montreal, Québec, Canada

    Google Scholar 

  6. Lahav O, Mioduser D (2003) A blind person’s cognitive mapping of new spaces using a haptic virtual environment. J Res Spec Educ Needs 3:172–177. doi:10.1111/1471-3802.00012

    Article  Google Scholar 

  7. Briscoe R, Grush R (2015) Action-based theories of perception. In: Stanford encyclopedia philosophy, pp 1–66

    Google Scholar 

  8. Foulke E (1982) Perception, cognition and the mobility of blind pedestrians. In: Potegal M (ed) Spatial Abilities: Development and physiological foundations, Academic Press, San Diego, CA, USA, pp 55-76

    Google Scholar 

  9. Loomis JM, Klatzky RL, Golledge RG, Cicinelli JG, Pellegrino JW, Fry PA (1993) Nonvisual navigation by blind and sighted: assessment of path integration ability. J Exp Psychol Gen 122:73–91. doi:10.1037/0096-3445.122.1.73

    Article  Google Scholar 

  10. Noordzij ML, Zuidhoek S, Postma A (2006) The influence of visual experience on the ability to form spatial mental models based on route and survey descriptions. Cognition 100:321–342. doi:10.1016/j.cognition.2005.05.006

    Article  Google Scholar 

  11. Rieser JJ, Lockman JJ, Pick HL (1980) The role of visual experience in knowledge of spatial layout. Percept Psychophys 28:185–190. doi:10.3758/BF03204374

    Article  Google Scholar 

  12. Gaunet F, Gentaz E (2009) Effect of visual experience on haptic estimations of spatial locations. L’année psychologique 109:237–252

    Article  Google Scholar 

  13. Rieser JJ, Guth DA, Hill EW (1986) Sensitivity to perspective structure while walking without vision. Perception 15:173–188

    Google Scholar 

  14. Hatwell Y (2003) Le développement perceptivo-moteur de l’enfant aveugle. Enfance 55:88–94

    Article  Google Scholar 

  15. Wirth KE, Rein DB (2008) The economic costs and benefits of dog guides for the blind. Ophthalmic Epidemiol 15:92–98. doi:10.1080/09286580801939353

    Article  Google Scholar 

  16. Li K (2015) Electronic travel aids for blind guidance. Presentation, University of California Berkeley

    Google Scholar 

  17. Murata (2008) Ultrasonic sensor application guide. 17

    Google Scholar 

  18. Russell L (1965) Travel path sounder. In: Proceedings of the rotterdam mobility research conference, American Foundation for the Blind, New York

    Google Scholar 

  19. Kay L (1964) An ultrasonic sensing probe as a mobility aid for the blind. Ultrasonics 2(2):53–59

    Google Scholar 

  20. GDP Research (2005) The miniguide mobility aid. http://www.gdp-research.com.au/minig_1.htm. Accessed 13 Sept 2016

  21. Pressey N (1977) Mowat sensor. Focus 11(3):35–39

    Google Scholar 

  22. Benet G, Blanes F, Simó JE, Pérez P (2002) Using infrared sensors for distance measurement in mobile robots. Robot Auton Syst 40:255–266. doi:10.1016/S0921-8890(02)00271-3

    Article  Google Scholar 

  23. Farcy R, Leroux R, Jucha A, Damaschini R, Grégoire C, Zogaghi A (2006) Electronic travel aids and electronic orientation aids for blind people: technical, rehabilitation and everyday life points of view. In: Hersh MA (ed) Conference & workshop on assistive technologies for people with vision & hearing impairments, Kufstein, Austria, 17–21 July 2006

    Google Scholar 

  24. Foerster K-T, Gross A, Hail N, Uitto J, Wattenhofer R (2014) SpareEye: enhancing the safety of inattentionally blind smartphone users. In: Proceedings of the 13th international conference on mobile and ubiquitous multimedia, CM, New York, NY, USA, pp 68–72

    Google Scholar 

  25. Karthick M, Suguna R (2015) Obstacle detection for visually impaired people using smart phones. Int J Emerg Technol Comput Sci Electron 13(1):530–535

    Google Scholar 

  26. Tapu R, Mocanu B, Bursuc A, Zaharia T (2013) A smartphone-based obstacle detection and classification system for assisting visually impaired people. In: 2013 IEEE international conference on computer vision workshop, pp 444–451

    Google Scholar 

  27. Yusro M, Hou KM, Pissaloux E, Shi HL, Ramli K, Sudiana D (2013) SEES: concept and design of a smart environment explorer stick. In: 2013 6th international conference on human system interaction HSI, pp 70–77

    Google Scholar 

  28. Lin XM, Connier J, Vaslin P, Guo CC, Zang TY, Li JJ, De Vaulx C, Hou KM (2015) Indoor navigation with the Smart Environment Explorer Stick (SEES). Paper presented at the 2015 new information communication sciences and technology workshop (NICST 2015), IMS, Bordeaux, France, 8–9 Sept 2015

    Google Scholar 

  29. Shoval S, Borenstein J, Koren Y (1993) The navbelt—a computerized travel aid for the blind. In: Proceedings of the RESNA ’93 conference, Las Vegas, Nevada, 12–17 June 1993

    Google Scholar 

  30. Ulrich I, Borenstein J (2001) The GuideCane–applying mobile robot technologies to assist the visually impaired. IEEE Trans Syst Man Cybern Part Syst Hum 31:131–136. doi:10.1109/3468.911370

    Article  Google Scholar 

  31. Mhajeri N, Raste R, Daneshvar S (2011) An obstacle detection system for blind People. In: Proceedings of the world congress on engineering 2011 vol II, London, U.K., 6–8 July 2011

    Google Scholar 

  32. Molton N, Se S, Brady JM, Lee D, Probert P (1998) A stereo vision-based aid for the visually impaired. Image Vis Comput 16:251–263. doi:10.1016/S0262-8856(97)00087-5

    Article  Google Scholar 

  33. Sharma PS, Chitaliya NG (2015) Obstacle avoidance using stereo vision: a survey. Int J Innovative Res Comput Commun Eng 3(1):24–29

    Article  Google Scholar 

  34. Lazaros N, Sirakoulis GC, Gasteratos A (2008) Review of stereo vision algorithms: from software to hardware. Int J Optomechatronics 2:435–462. doi:10.1080/15599610802438680

    Article  Google Scholar 

  35. Yusro M, Hou K-M, Pissaloux E, Ramli K, Sudiana D, Zhang L-Z, Shi H-L (2014) Concept and design of SEES (Smart Environment Explorer Stick) for visually impaired person mobility assistance. Hum Comput Syst Interact Backgrounds Appl 300(3):245–259

    Google Scholar 

  36. LIDAR: https://en.wikipedia.org/wiki/Lidar. Accessed 8 Aug 2016

  37. Aguerrevere D, Choudhury M, Barreto A (2004) Portable 3D sound/soar navigation system for blind individuals. In: Second LACCEI international Latin American and Caribbean conference for engineering and technology

    Google Scholar 

  38. Parseihian G, Brilhault A, Dramas F (2010) Navig: an object localization system for the blind. In: Workshop pervasive 2010: multimodal location based techniques for extreme navigation, Helsinki

    Google Scholar 

  39. Filipe V, Fernandes F, Fernandes H, Sousa A, Paredes H, Barroso J (2012) Blind navigation support system based on microsoft kinect. Procedia Comput Sci 14:94–101. doi:10.1016/j.procs.2012.10.011

    Article  Google Scholar 

  40. European Commission (2010) Intelligent transport systems. Directorate-General for Research, Publications Office of the European Union, Luxembourg, 28 p

    Google Scholar 

  41. Wu H, Siegel M, Khosla P (1998) Vehicle sound signature recognition by frequency vector principal component analysis. In: Proceedings of IEEE instrumentation and measurement technology conference, 1998. IMTC 98, vol 1. pp 429–434

    Google Scholar 

  42. Lymberopoulos D, Liu J, Yang X, Choudhury RR, Handziski V, Sen S (2015) A realistic evaluation and comparison of indoor location technologies: experiences and lessons learned. In: Proceedings of the 14th international conference on information processing in sensor networks. ACM, Seattle, Washington, pp 178–189

    Google Scholar 

  43. Terrier P, Turner V, Schutz Y (2005) GPS analysis of human locomotion: further evidence for long-range correlations in stride-to-stride fluctuations of gait parameters. Hum Mov Sci 24:97–115. doi:10.1016/j.humov.2005.03.002

    Article  Google Scholar 

  44. Beder C, Klepal M (2012) Fingerprinting based localisation revisited: a rigorous approach for comparing RSSI measurements coping with missed access points and differing antenna attenuations. In: 2012 international conference on indoor positioning and indoor navigation (IPIN), pp 1–7

    Google Scholar 

  45. Li L, Hu P, Peng C, Shen G, Zhao F (2014) Epsilon: a visible light based positioning system. In: Proceedings of 11th USENIX conference on networked systems design and implementation, USENIX Association, Seattle, WA, pp 331–343

    Google Scholar 

  46. Riehle TH, Anderson SM, Lichter PA, Condon JP, Sheikh SI, Hedin DS (2011) Indoor waypoint navigation via magnetic anomalies. In: Annual international conference of the IEEE engineering in medicine and biology society EMBC 2011, pp 5315–5318

    Google Scholar 

  47. Sabatini AM (2005) Quaternion-based strap-down integration method for applications of inertial sensing to gait analysis. Med Biol Eng Comput 43:94–101

    Article  Google Scholar 

  48. de Saint Rémy N, Vaslin P, Dabonneville M, Roux D, Hou KM, Cid M (2003) Can the distance run by a wheelchair be calculated from the measurements of a 3-D accelerometer? Actes du XXVIIIème Congrès de la Société de Biomécanique, Poitiers, 11–12 Sept, Arch Physiol Biochem 111(suppl.):99

    Google Scholar 

  49. Vaslin P, Dabonneville M (2000) Use of a 3D accelerometer for kinetic analysis of wheelchair propulsion. In: Proceedings of the 12th conference of the european society of biomechanics, Trinity College, Dublin (Ireland), 28–30 Aug, p 345

    Google Scholar 

  50. Faroux JP, Renault J (1996) Mécanique 1: point et systèmes de points (1ère année MPSI – PCSI). Dunod [Collection J’intègre – Prépas scientifiques], 4ème édition, Paris, 372 p

    Google Scholar 

  51. Grossetête C, Olive P (1999) Mécanique des systèmes et du solide – Cours et exercices corrigés (2ème année MP – MP* – PT – PT*), Ellipses/Édition Marketing S.A. [Collection Taupe-Niveau – Classes préparatoires aux Grandes Écoles Scientifiques], Paris, 312 p

    Google Scholar 

  52. Radix JC (1978) Gyroscopes et gyromètres. Sup’ Aéro: École Nationale Supérieure de l’Aéronautique et de l’Espace, Toulouse: Cépaduès Éditions, 390 p

    Google Scholar 

  53. Animazoo Uk Ltd (2006) Quayside offices. Basin Road South, Brighton, East Sussex, BN41 1WF. http://www.animazoo.com/contact/. Accessed 15 Sept 2016

  54. Cutti AG, Giovanardi A (2008) Ambulatory measurement of shoulder and elbow kinematics through inertial and magnetic sensors. Med Biol Eng Comput 46:169–178

    Article  Google Scholar 

  55. Cutti AG, Ferrari A (2010) ‘Outwalk’: a protocol for clinical gait analysis based on inertial and magnetic sensors. Med Biol Eng Comput 48:17–25

    Article  Google Scholar 

  56. Ferrari A, Cutti AG, Garofalo P, Raggi M, Heijboer M, Cappello A, Davalli A (2010) First in vivo assessment of ‘‘Outwalk’’: a novel protocol for clinical gait analysis based on inertial and magnetic sensors. Med Biol Eng Comput 48:1–15

    Article  Google Scholar 

  57. Luinge HJ, Veltink PH (2005) Measuring orientation of human body segments using miniature gyroscopes and accelerometers. Med Biol Eng Comput 43(2):273–282

    Article  Google Scholar 

  58. Luinge HJ, Veltink PH, Baten CTM (2007) Ambulatory measurement of arm orientation. J Biomech 40(1):78–85

    Article  Google Scholar 

  59. Picerno P, Cereatti A, Cappozzo A (2008) Joint kinematics estimate using wearable inertial and magnetic sensing modules. Gait Posture 28(4):588–595

    Article  Google Scholar 

  60. Picerno P, Cereatti A, Cappozzo A (2011) A spot check for assessing static orientation consistency of inertial and magnetic sensing units. Gait Posture 33:373–378

    Article  Google Scholar 

  61. deVries WHK, Veeger HEJ, Cutti AG, Baten C, Van der Helm FCT (2010) Functionally interpretable local coordinate systems for the upper extremity using inertial & magnetic measurement systems. J Biomech 43:1983–1988

    Article  Google Scholar 

  62. Williamson R, Andrews BJ (2001) Detecting absolute human knee angle and angular velocity using accelerometers and rate gyroscopes. Med Biol Eng Compu 39:1–9

    Article  Google Scholar 

  63. Xsens Technologies (2009) Pros and cons of inertial sensing for human motion analysis. 2 Nov 2009. Webinar Xsens. http://download.xsens.com/XsensProsCons20091105.wmv. PowerPoint presentation. http://download.xsens.com/XsensProsConsInertialSensing20091105.pdf

  64. Naqvib NZ, Kumar A, Chauhan A, Sahni K (2012) Step counting using smartphone-based accelerometer. Int J Comput Sci Eng 4:675

    Google Scholar 

  65. Zheng X, Yang H, Tang W, Pu S, Zheng L, Zheng H, Liao B, Wang J (2014) Indoor pedestrian navigation with shoe-mounted inertial sensors. In: Park JJJH, Chen S-C, Gil J-M, Yen YN (eds) Multimedia and ubiquitous engineering, Springer, Berlin, pp 67–73

    Google Scholar 

  66. Gaunet F, Briffault X (2005) Exploring the functional specifications of a localized wayfinding verbal aid for blind pedestrians: simple and structured urban areas. Hum Comput Interact 20:267–314. doi:10.1207/s15327051hci2003_2

    Article  Google Scholar 

  67. Matthews B, Hibberd D, Carsten O (2014) Road and street crossings for blind and partially sighted people: the importance of being certain. Institute for Transport Studies, University of Leeds

    Google Scholar 

  68. Wang S, Tian Y (2012) Detecting stairs and pedestrian crosswalks for the blind by RGBD camera. In: 2012 IEEE international conference on bioinformatics and biomedicine workshops (BIBMW), pp 732–739

    Google Scholar 

  69. Ivanchenko V, Coughlan J, Shen H (2008) Detecting and locating crosswalks using a camera phone. In: Proceedings of CVPR IEEE computer society conference on computer vision and pattern recognition 2008:4563143. doi:10.1109/CVPRW.2008.4563143

  70. Ivanchenko V, Coughlan J, Shen H (2009) Staying in the crosswalk: a system for guiding visually impaired pedestrians at traffic intersections. Assist Technol Res Ser 25:69–73. doi:10.3233/978-1-60750-042-1-69

    Google Scholar 

  71. Wang S, Tian Y (2011) Indoor signage detection based on saliency map and bipartite graph matching. In: 2011 IEEE international conference on bioinformatics and biomedicine workshops (BIBMW), pp 518–525

    Google Scholar 

  72. Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of Things (IoT): a vision, architectural elements, and future directions. Future Gener Comput Syst 29(7):1645–1660

    Article  Google Scholar 

  73. Tian Y, Yang X, Arditi A (2010) Computer vision-based door detection for accessibility of unfamiliar environments to blind persons. In: Miesenberger K, Klaus J, Zagler W, Karshmer A (eds) Computers helping people with special needs. Springer, Berlin, pp 263–270

    Google Scholar 

  74. Yi C, Flores RW, Chincha R, Tian Y (2013) Finding objects for assisting blind people. Netw Model Anal Health Inform Bioinforma 2:71–79. doi:10.1007/s13721-013-0026-x

    Article  Google Scholar 

  75. Jung K, In Kim K, Jain KA (2004) Text information extraction in images and video: a survey. Pattern Recognit 37:977–997. doi:10.1016/j.patcog.2003.10.012

    Article  Google Scholar 

  76. Yi C, Tian Y (2014) Scene text recognition in mobile applications by character descriptor and structure configuration. IEEE Trans Image Process 23:2972–2982. doi:10.1109/TIP.2014.2317980

    Article  MathSciNet  Google Scholar 

  77. Buckley JJ, Siler W, Tucker D (1986) A fuzzy expert system. Fuzzy Sets Syst 20:1–16. doi:10.1016/S0165-0114(86)80027-6

    Article  Google Scholar 

  78. Fasel B, Luettin J (2003) Automatic facial expression analysis: a survey. Pattern Recognit 36:259–275. doi:10.1016/S0031-3203(02)00052-3

    Article  MATH  Google Scholar 

  79. Martinez F, Carbone A, Pissaloux E (2013) Combining first-person and third‐person gaze for attention recognition. In: 10th IEEE conference on automatic face and gesture recognition, Shanghai

    Google Scholar 

  80. Yang M, Yu K (2011) Real-time clothing recognition in surveillance videos. In: 2011 18th IEEE international conference on image processing ICIP, pp 2937–2940

    Google Scholar 

  81. Kotus J, Lopatka K, Czyzewski A (2012) Detection and localization of selected acoustic events in acoustic field for smart surveillance applications. Multimed Tools Appl 68:5–21. doi:10.1007/s11042-012-1183-0

    Article  Google Scholar 

  82. Schwarz LA, Mkhitaryan A, Mateus D, Navab N (2011) Estimating human 3D pose from time-of-flight images based on geodesic distances and optical flow. In: 2011 IEEE international conference on automatic face & gesture recognition, Workshop FG 2011, pp 700–706

    Google Scholar 

  83. Randell B, Avizienis A (2001) Fundamental concepts of dependability. LAAS-CNRS

    Google Scholar 

  84. Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey. Comput Netw 38:393–422. doi:10.1016/S1389-1286(01)00302-4

    Article  Google Scholar 

  85. Akyildiz IF, Melodia T, Chowdhury KR (2008) Wireless multimedia sensor networks: applications and testbeds. Proc IEEE 96:1588–1605. doi:10.1109/JPROC.2008.928756

    Article  Google Scholar 

  86. Hamdi M, Boudriga N, Obaidat MS (2008) Bandwidth-effective design of a satellite-based hybrid wireless sensor network for mobile target detection and tracking. IEEE Syst J 2:74–82. doi:10.1109/JSYST.2007.916049

    Article  Google Scholar 

  87. Munir A, Gordon-Ross A, Ranka S (2015) Modeling and optimization of parallel and distributed embedded systems. Wiley, USA

    Google Scholar 

  88. Munir A, Gordon-Ross A, Ranka S (2014) Multi-core embedded wireless sensor networks: architecture and applications. IEEE Trans Parallel Distrib Syst 25:1553–1562. doi:10.1109/TPDS.2013.219

    Article  Google Scholar 

  89. Shi HL, Hou KM, Zhou HY, Liu X (2011) Energy efficient and fault tolerant multicore wireless sensor network: E2MWSN. In: 2011 7th international conference on wireless communications, networking and mobile computing WiCOM, pp 1–4

    Google Scholar 

  90. Shi H-L, Hou K-M, Diao X, Xing L, De Vaulx C (2013) A robust multi-core multi-support and modular wireless multimedia sensor network: MiLive. ECOTECHS’2013, Montoldre, France, 9–10 Oct 2013

    Google Scholar 

  91. Kleihorst R, Schueler B, Danilin A, Heijligers M (2006) Smart camera mote with high performance vision system. In: Workshop on distributed smart cameras (DSC), Boulder, Colorado, Oct 2006

    Google Scholar 

  92. Rasberry Pi Foundation (2016) Raspberry Pi 1 model B. http://www.raspberry.org/products/model-b/. Accessed 18 Mar 2016

  93. SMIR Group LIMOS UMR 6158 CNRS Blaise Pascal University (2016) iLive platform introduction. http://edss.isima.fr/auth/home.php, 2016. Accessed 18 Mar 2016

  94. Duffy C, Roedig U, Herbert J, Sreenan C (2008) A comprehensive experimental comparison of event driven and multi-threaded sensor node operating systems. J Netw. doi:10.4304/jnw.3.3.57-70

    Google Scholar 

  95. Bhatti S, Carlson J, Dai H, Deng J, Rose J, Sheth A, Shucker B, Gruenwald C, Torgerson A, Han R (2005) MANTIS OS: an embedded multithreaded operating system for wireless micro sensor platforms. Mob Netw Appl 10:563–579

    Article  Google Scholar 

  96. De Vaulx C, Hou K-M (2002) DREAM: un micro noyau temps réel orienté pour la tolérance aux fautes. Informatique et Santé 13:63–69

    Google Scholar 

  97. Zhou H, Hou KM, De Vaulx C (2006) SDREAM : a super-small distributed real-time microkernel dedicated to wireless sensors. JPCC Spec Issue Key Technol Appl Wirel Sens Body-Area Netw 12 p

    Google Scholar 

  98. Hill J, Szewczyk R, Woo A, Hollar S, Culler D, Pister K (2000) System architecture directions for networked sensors. In: Proceedings of ninth international conference on architectural support for programming languages and operating systems, ACM, New York, NY, USA, pp 93–104

    Google Scholar 

  99. Dunkels A, Gronvall B, Voigt T (2004) Contiki—a lightweight and flexible operating system for tiny networked sensors. In: 29th annual IEEE international conference on local computer networks 2004, pp 455–462

    Google Scholar 

  100. Han C, Kumar R, Shea R, Kohler E, Srivastava M (2005) A dynamic operating system for sensor nodes. In: Proceedings of the mobile systems, applications, and services (MobiSys), ACM, p 117–124

    Google Scholar 

  101. Klues K, Liang CJM, Paek J, Musaloiu-Elefteri R, Levis P, Terzis A, Govindan R (2009) TOSThreads: Thread-safe and non-invasive preemption in TinyOS. In: Proceedings of the 7th ACM conference on embedded networked sensor systems, ACM, p 127–140

    Google Scholar 

  102. Liu X, Hou KM, de Vaulx C, Shi H, Gholami KE (2014) MIROS: a hybrid real-time energy-efficient operating system for the resource-constrained wireless sensor nodes. Sensors 14:17621–17654. doi:10.3390/s140917621

    Article  Google Scholar 

  103. Xenomai (2016) About Xenomai. https://xenomai.org/about-xenomai/. Accessed 20 Mar 2016

  104. Kushalnagar N, Montenegro G, Schumacher C (2007) IPv6 over Low-Power Wireless Personal Area Networks (6LoWPANs): overview, assumptions, problem statement, and goals. https://tools.ietf.org/html/rfc4919, Accessed 18 Mar 2016

  105. Montenegro G, Kushalnagar N, Hui J, Culler D (2007) Transmission of IPv6 packets over IEEE 802.15.4 networks. https://tools.ietf.org/html/rfc4944. Accessed 18 Mar 2016

  106. Winter T, Thubert P, RPL Author Team (2012) RPL: IPv6 routing protocol for low power and lossy networks. https://tools.ietf.org/html/rfc6550. Accessed 18 Mar 2016

  107. Shelby Z, Hartke K, Bormann C (2014) The Constrained Application Protocol (CoAP). https://tools.ietf.org/html/rfc7252. Accessed 18 Mar 2016

  108. OASIS (2014) MQTT version 3.1.1. http://docs.oasis-open.org/mqtt/mqtt/v3.1.1/os/mqtt-v3.1.1-os.html. Accessed 18 Mar 2016

  109. Fielding RT (2000) Architectural styles and the design of network-based software architectures. Ph.D. dissertation, Irvin University

    Google Scholar 

  110. Durrant-Whyte HF (1988) Sensor models and multisensor integration. Int J Robot Res 7:97–113. doi:10.1177/027836498800700608

    Article  Google Scholar 

  111. Wu H (2004) Sensor data fusion for context-aware computing using dempster-shafer theory. Carnegie Mellon University, Pittsburgh

    Google Scholar 

  112. W School Josm. http://josm.openstreetmap.de

  113. Maperitive. http://maperitive.net/

  114. Easy CAD to SVG converter. http://dwg2svg.en.softonic.com/

  115. Inkscape. https://inkscape.org/en/

  116. Dorigo M, Stützle T (2010) Ant colony optimization: overview and recent advances. In: Gendreau M, Potvin J-Y (eds) Handbook of metaheuristics, Springer US, pp 227–263

    Google Scholar 

  117. Chang Q, Wang W, Li Q, Towards automatic context-sense for seamless navigation and localization using smart phone sensors. Sensors (to be published)

    Google Scholar 

  118. Vuforia. http://www.vuforia.com/

  119. RocketMQ. https://github.com/alibaba/RocketMQ

  120. Apache Storm. http://storm.apache.org/

  121. Docker. https://www.docker.com/

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Motta, G. et al. (2018). Overview of Smart White Canes: Connected Smart Cane from Front End to Back End. In: Pissaloux, E., Velazquez, R. (eds) Mobility of Visually Impaired People. Springer, Cham. https://doi.org/10.1007/978-3-319-54446-5_16

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