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Design and development of an indoor navigation system using denoising autoencoder based convolutional neural network for visually impaired people

  • 1188: Artificial Intelligence for Physical Agents
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Abstract

A challenging area of research is the development of a navigation system for visually impaired people in an indoor environment such as a railway station, commercial complex, educational institution, and airport. Identifying the current location of the users can be a difficult task for those with visual impairments. The entire selection of the navigation path depends upon the current location of the user. This work presents a detailed analysis of the recent user positioning techniques and methodologies on the indoor navigation system based on the parameters, such as techniques, cost, the feasibility of implementation, and limitations. This paper presents a denoising auto encoder based on the convolutional neural network (DAECNN) to identify the present location of the users. The proposed approach uses the de-noising autoencoder to reconstruct the noisy image and the convolution neural network (CNN) to classify the users' current position. The proposed method is compared with the existing deep learning approaches such as deep autoencoder, sparse autoencoder, CNN, multilayer perceptron, radial basis function neural network, and the performances are analyzed. The experimental findings indicate that the DAECNN methodology works better than the existing classification approaches.

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References

  1. Afif M, Ayachi R, Pissaloux E, Said Y, Atri M (2020) Indoor objects detection and recognition for an ICT mobility assistance of visually impaired people. Multimed Tools Appl 79(41):31645–31662

    Article  Google Scholar 

  2. Ahmad M, Shabbir S, Oliva D, Mazzara M, Distefano S (2020) Spatial-prior generalized fuzziness extreme learning machine autoencoder-based active learning for hyperspectral image classification. Optik 206(163712):1–8

    Google Scholar 

  3. Akilandeswari J, Naveenkumar A, Sabeenian RS, Iyyanar P, Paramasivam ME, Jothi G (2021) An investigation on indoor navigation systems. Evolution in computational intelligence. Springer, Singapore, pp 115–124

    Chapter  Google Scholar 

  4. Akilandeswari J, Jothi G, Naveenkumar A, Sabeenian RS, Iyyanar P, Paramasivam ME (2021) Detecting pulmonary embolism using deep neural networks. Int J Perform Eng 17(3):322–332

    Article  Google Scholar 

  5. Bonde GD, Barwal PU, Pal SR, Khan SI, Ablankar K (2015) Finding indoor position of person using wi-fi & smartphone: a survey. Int J Innov Res Sci Technol 1(8):202–207

    Google Scholar 

  6. Cardoso P, Domingos D, Cláudio AP (2016) Indoor navigation systems for reduced mobility users: the w4all case study. Proc Comput Sci 100:1200–1207

    Article  Google Scholar 

  7. Chan S, Sohn G (2012) Indoor localization using wi-fi based fingerprinting and trilateration techniques for lbs applications. Int Arch Photogramm Remote Sens Spatial Inf Sci 38(4):1–5

    Article  Google Scholar 

  8. Cheng R, Wang K, Lin S, Hu W, Yang K, Huang X, Bai J (2019) Panoramic annular localizer: tackling the variation challenges of outdoor localization using panoramic annular images and active deep descriptors. In: 2019 IEEE Intelligent Transport Syst Conf (ITSC) 920–925

  9. Ciezkowski M (2017) Triangulation positioning system based on a static IR beacon-receiver system. In: 2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR) 84–8

  10. Do T-H, Yoo M (2016) An in-depth survey of visible light communication based positioning systems. Sensors 16(5):678

    Article  Google Scholar 

  11. Dourado AMB, Pedrino EC (2020) Multi-objective cartesian genetic programming optimization of morphological filters in navigation systems for visually impaired people. Appl Soft Comput 89(106130):1–13

    Google Scholar 

  12. Fang Y, Yang K, Cheng R, Sun L, Wang K (2020) A panoramic localizer based on coarse-to-fine descriptors for navigation assistance. Sensors 20(15):4177

    Article  Google Scholar 

  13. Farshad H, Zare Zardiny A (2014) Location based service in indoor environment using quick response code technology. Int Arch Photogramm Remote Sens Spatial Inf Sci 40(2):137

    Google Scholar 

  14. Fath AH, Madanifar F, Abbasi M (2020) Implementation of multilayer perceptron (MLP) and radial basis function (RBF) neural networks to predict solution gas-oil ratio of crude oil systems. Petroleum 6(1):80–91

    Article  Google Scholar 

  15. Gobi R (2021) Smartphone based indoor localization and tracking model using bat algorithm and Kalman filter. Multim Tools Appl 80(10):15377–15390

    Article  Google Scholar 

  16. Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554

    Article  MathSciNet  MATH  Google Scholar 

  17. Ilkovičová Ľ, Erdélyi J, Kopáčik A (2014) Positioning in indoor environment using QR codes. In: International Conference on Engineering Surveying Prague, Czech republic (INGEO) 117–122

  18. Ivanov R (2010) Indoor navigation system for visually impaired. In: Proc 11th Intern Conf Comp Syst Technol Workshop PhD Students Comput Intern Conf Comp Syst Technol 143–149

  19. Jeyapal A, Ganesan J, Savarimuthu SR, Perumal I, Eswaran PM, Subramanian L, Anbalagan N (2020) A comparative study of feature detection techniques for navigation of visually impaired person in an indoor environment. J Comput Theor Nanosci 17(1):21–26

    Article  Google Scholar 

  20. Jun Qi, Liu G-P (2017) A robust high-accuracy ultrasound indoor positioning system based on a wireless sensor network. Sensors 11(2554):1–17

    Google Scholar 

  21. Karim AM, Güzel MS, Tolun MR, Kaya H, Çelebi FV (2019) A new framework using deep auto-encoder and energy spectral density for medical waveform data classification and processing. Biocybern Biomed Eng 39(1):148–159

    Article  Google Scholar 

  22. Kirchner N, Furukawa T (2005) Infrared localisation for indoor uavs. In: Proc 1st Intern Conf Sens Technol

  23. Kassim AM, Yasuno T, Suzuki H, Jaafar HI, Aras MSM (2016) Indoor navigation system based on passive RFID transponder with digital compass for visually impaired people. Int J Adv Comput Sci Appl 7(2):604–611

    Google Scholar 

  24. Lachenbruch PA, Lynch CJ (1998) Assessing screening tests: extensions of McNemar’s test. Stat Med 17(19):2207–2217

    Article  Google Scholar 

  25. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  26. Lee BH, Lee YJ (2019) Evaluation of medication use and pharmacy services for visually impaired persons: perspectives from both visually impaired and community pharmacists. Disabil Health J 12(1):79–86

    Article  Google Scholar 

  27. Li W, Liu X, Liu J, Chen P, Wan S, Cui X (2019) On improving the accuracy with auto-encoder on conjunctivitis. Appl Soft Comput 81(105489):1–11

    Google Scholar 

  28. Lin S, Cheng R, Wang K, Yang K (2018) Visual localizer: outdoor localization based on convnet descriptor and global optimization for visually impaired pedestrians. Sensors 18(8):2476

    Article  Google Scholar 

  29. Liu W, Ma T, Xie Q, Tao D, Cheng J (2017) LMAE: a large margin auto-encoders for classification. Signal Process 141:137–143

    Article  Google Scholar 

  30. Mahiddin NA, Safie N, Nadia E, Safei S, Fadzli E (2012) Indoor position detection using WiFi and trilateration technique. In: The International Conference on Informatics and Applications (ICIA2012) 362–366

  31. Mallick PK, Ryu SH, Satapathy SK, Mishra S, Nguyen GN, Tiwari P (2019) Brain MRI image classification for cancer detection using deep wavelet autoencoder-based deep neural network. IEEE Access 7:46278–46287

    Article  Google Scholar 

  32. Manjari K, Verma M, Singal G (2020) A survey on assistive technology for visually impaired. Internet Things 100188:1–20

    Google Scholar 

  33. Makhzan A,Frey B (2013) K-sparse autoencoders. arXiv preprint https://arxiv.org/abs/1312.5663. 1–9

  34. Mashuk MS, Pinchin J, Siebers PO, Moore T (2018) A smart phone-based multi-floor indoor positioning system for occupancy detection. In: 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS) 216–227

  35. Mashuk MS, Pinchin J, Siebers PO, Moore T (2018) A smart phone based multi-floor indoor positioning system for occupancy detection. In: 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS) 216–227

  36. Medina C, Segura JC, De la Torre A (2013) Ultrasound indoor positioning system based on a low-power wireless sensor network providing sub-centimeter accuracy. Sensors 3:3501–3526

    Article  Google Scholar 

  37. Meliones A, Sampson D (2018) Blind museumtourer: a system for self-guided tours in museums and blind indoor navigation. Technologies 6(1):4

    Article  Google Scholar 

  38. Nabney IT (2004) Efficient training of RBF networks for classification. Int J Neural Syst 14(03):201–208

    Article  Google Scholar 

  39. Nagarajan B, Shanmugam V, Ananthanarayanan V, Sivakumar PB (2020) Localization and indoor navigation for visually impaired using bluetooth low energy. Smart systems and IoT: innovations in computing. Springer, Singapore, pp 249–259

    Chapter  Google Scholar 

  40. Nakajima M, Haruyama S (2013) New indoor navigation system for visually impaired people using visible light communication. EURASIP J Wirel Commun Netw 2013(1):37

    Article  Google Scholar 

  41. Ng A (2011) Sparse autoencoder. CS294A Lecture notes 72:1–19

  42. Ovchinnikov IA, Kudryavtsev KY (2020) Indoor positioning system based on mobile devices. Advanced technologies in robotics and intelligent systems. Springer, Cham, pp 285–289

    Chapter  Google Scholar 

  43. Potgantwar A, Kohli JK, Shirke P (2015) Adaptive RSS based indoor positioning system using RFID and wireless technology. Glob J Adv Eng Technol 4(4):436–446

    Google Scholar 

  44. Qi J, Liu GP (2017) A robust high-accuracy ultrasound indoor positioning system based on a wireless sensor network. Sensors 17(11):1–17

    Article  Google Scholar 

  45. Roy P, Chowdhury C (2021) Designing an ensemble of classifiers for smartphone-based indoor localization irrespective of device configuration. Multimed Tools Appl 80:1–25

    Article  Google Scholar 

  46. Sakai N, Zempo K, Mizutani K, Wakatsuki N (2016) linear positioning system based on ir beacon and angular detection photodiode array. In: Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), Alcalá de Henares. Spain 4–7

  47. Sarimveis H, Doganis P, Alexandridis A (2006) A classification technique based on radial basis function neural networks. Adv Eng Softw 37(4):218–221

    Article  Google Scholar 

  48. Shahid E, Arain QA (2021) Indoor positioning: an image-based crowdsource machine learning approach. Multimed Tools Appl 2021:1–23

    Google Scholar 

  49. Taheri A, Singh A, Emmanuel A (2004) Location fingerprinting on infrastructure 802.11 wireless local area networks (WLANs) using Locus. In 29th Annual IEEE Intern Conf Local Comp Network 676–683

  50. Tandon K, Pande T, Adil M, Dubey G, Kumar A (2015) A blind navigation system using RFID for indoor environments. Int J Comput Syst 2(4):115–118

    Google Scholar 

  51. Tang J, Deng C, Huang GB (2015) Extreme learning machine for multilayer perceptron. IEEE Trans Neural Netw Learn Syst 27(4):809–821

    Article  MathSciNet  Google Scholar 

  52. Thoma J, Paude DP, Chhatkuli A, Probst T, Gool LV (2019) Mapping, localization, and path planning for image-based navigation using visual features and map. In: Proc IEEE/CVF Conf Comp Vision Pattern Recogn  7383–7391

  53. Tom M (1977) Machine learning. McGraw Hill, Burr Ridge, pp 870–877

    Google Scholar 

  54. Tsirmpas C, Rompas A, Fokou O, Koutsouris D (2015) An indoor navigation system for visually impaired and elderly people based on radio frequency identification (RFID). Inf Sci 320:288–305

    Article  Google Scholar 

  55. Walch F, Hazirbas C, Leal-Taixe L, Sattler T, Hilsenbec S, Cremers D (2017) Image-based localization using LSTMs for structured feature correlation. In: Proc the IEEE Intern Conf Comp Vision 627–637

  56. Wang J, Perez L (2017) The effectiveness of data augmentation in image classification using deep learning. Convolutional Neural Netw Vis Recognit 11:1–8

    Google Scholar 

  57. Wang W, Chang Q, Li Q, Shi Z, Chen W (2016) Indoor-outdoor detection using a smart phone sensor. Sensors 16(10):1563

    Article  Google Scholar 

  58. Waqar W, Chen Y, Vardy A (2011) Exploiting smartphone sensors for indoor positioning: a survey. In: Proc Newfoundland Conf Electric Comp Eng 1–5

  59. Werner M, Kessel M, Marouane C (2011) Indoor positioning using smartphone camera. In: 2011 Intern Conf Indoor Position indoor Navigation 1–6

  60. Willis S, Helal S (2005) RFID information grid for blind navigation and wayfinding. In: Ninth IEEE International Symposium on Wearable Computers (ISWC'05) 34–37

  61. World Health Organization (2020) https://www.who.int/news-room/fact-sheets/detail/blindness-and-visual-impairment. Accessed 3 Sept 2020

  62. Xiao A, Chen R, Li D, Chen Y, Wu D (2018) An indoor positioning system based on static objects in large indoor scenes by using smartphone cameras. Sensors 18(7):1–17

    Article  Google Scholar 

  63. Xie Bo, Chen K, Tan G, Mingming Lu, Liu Y, Jie Wu, He T (2016) LIPS: a light intensity–based positioning system for indoor environments. ACM Transactions on Sensor Networks (TOSN) 12(4):1–27

    Article  Google Scholar 

  64. Vincent P, Larochelle H, Bengio Y, Manzagol PA (2008. Extracting and composing robust features with denoising autoencoders. In: Proc 25th Intern Conf Machine Learning 1096–1103

  65. Yayan U, Yucel H (2015) A low cost ultrasonic based positioning system for the indoor navigation of mobile robots. J Intell Rob Syst 78(3–4):541–552

    Article  Google Scholar 

  66. Zhang Y, Zhang E, Chen W (2016) Deep neural network for halftone image classification based on sparse auto-encoder. Eng Appl Artif Intell 50:245–255

    Article  Google Scholar 

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Funding

This research work is done as part of the research grant from Science for Equity Empowerment and Development Division (SEED), Department of Science & Technology, Technology Bhavan, New Mehrauli Road, New Delhi under SEED//TIDE/202/2016/G, dated: 12/01/2018.

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Authors and Affiliations

  1. Dept. of IT, Sona College of Technology, Salem, 636005, Tamil Nadu, India

    J. Akilandeswari, A. Naveenkumar & P. Iyyanar

  2. Dept. of BCA, Sona College of Arts and Science, Salem, 636005, Tamil Nadu, India

    G. Jothi

  3. Dept. of ECE, Sona College of Technology, Salem, 636005, Tamil Nadu, India

    R. S. Sabeenian & M. E. Paramasivam

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  1. J. Akilandeswari
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  2. G. Jothi
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  6. M. E. Paramasivam
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Correspondence to J. Akilandeswari.

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Akilandeswari, J., Jothi, G., Naveenkumar, A. et al. Design and development of an indoor navigation system using denoising autoencoder based convolutional neural network for visually impaired people. Multimed Tools Appl 81, 3483–3514 (2022). https://doi.org/10.1007/s11042-021-11287-z

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