Skip to main content
SpringerLink
Log in

Smart Device-Based PDR Methods for Indoor Localization

  • Chapter
  • First Online:
Machine Learning for Indoor Localization and Navigation

  • 462 Accesses

Abstract

Smart devices, such as smartphones and smartwatches, are indispensable nowadays for everyone’s daily life due to their mobility and powerful computation capability. Sensors embedded in these devices are relatively low-cost and convenient to carry. Consequently, leveraging the sensors embedded in smart devices has provided new opportunities for indoor PDR developments. In this chapter, we first introduce various types of smart devices and device-based carrying modes. We then describe the types and functionalities of sensors built into these devices, as well as common steps and evaluation metrics in smart device-based PDR methods. Several methods are summarized based on the usage of smart devices, sensors, techniques, and performances. Lastly, we present challenges and issues that remain for current smart device-based PDR methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 119.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.waseda.jp/inst/research/news/55594

References

  1. Bahl P, Padmanabhan VN (2000) RADAR: an in-building RF-based user location and tracking system. In: Proceedings of the IEEE Conference on Computer and Communications, INFOCOM, pp 775–784

    Google Scholar 

  2. Bao H, Wong WC (2013) Improved PCA based step direction estimation for dead-reckoning localization. In: Proceedings of the International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC, pp 325–331

    Google Scholar 

  3. Bylemans I, Weyn M, Klepal M (2009) Mobile phone-based displacement estimation for opportunistic localisation systems. In: Proceedings of the International Conference on Mobile Ubiquitous Computing, Systems, Services, and Technologies, UBICOMM, pp 113–118

    Google Scholar 

  4. Ciabattoni L, Foresi G, Monteriù A, Pepa L, Pagnotta DP, Spalazzi L, Verdini F (2019) Real time indoor localization integrating a model based pedestrian dead reckoning on smartphone and BLE beacons. J Ambient Intell Humaniz Comput 10(1):1–12

    Article  Google Scholar 

  5. Correa A, Munoz Diaz E, Ahmed DB, Morell A, Lopez Vicario J (2016) Advanced pedestrian positioning system to smartphones and smartwatches. Sensors 16(11):1–18

    Article  Google Scholar 

  6. Deng ZA, Wang G, Hu Y, Wu D (2015) Heading estimation for indoor pedestrian navigation using a smartphone in the pocket. Sensors 15(9):21518–21536

    Article  Google Scholar 

  7. Harle R (2013) A survey of indoor inertial positioning systems for pedestrians. IEEE Commun Surv Tutorials 15(3):1281–1293

    Article  Google Scholar 

  8. Hightower J, LaMarca A, Smith IE (2006) Practical lessons from place lab. IEEE Pervasive Comput 5(3):32–39

    Article  Google Scholar 

  9. Ju H, Park SY, Park CG (2018) A smartphone-based pedestrian dead reckoning system with multiple virtual tracking for indoor navigation. IEEE Sensors J 18(16):6756–6764

    Article  Google Scholar 

  10. Kang W, Nam S, Han Y, Lee S (2012) Improved heading estimation for smartphone-based indoor positioning systems. In: Proceedings of the IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, pp 2449–2453

    Google Scholar 

  11. Kang W, Han Y (2015) SmartPDR: smartphone-based pedestrian dead reckoning for indoor localization. IEEE Sensors J 15(5):2906–2916

    Article  Google Scholar 

  12. Klein I, Solaz Y, Ohayon G (2017) Smartphone motion mode recognition. IEEE Sensors J 18(18):7577–7584

    Article  Google Scholar 

  13. Krumm J, Harris S, Meyers B, Brumitt B, Hale M, Shafer S (2000) Multi-camera multi-person tracking for EasyLiving. In: Proceedings of the IEEE International Workshop on Visual Surveillance, pp 3–10

    Google Scholar 

  14. Kumar S, Gil S, Katabi D, Rus D (2014) Accurate indoor localization with zero start-up cost. In: Proceedings of the ACM Annual International Conference on Mobile Computing and Networking, MobiCom, pp 483–494

    Google Scholar 

  15. Lee JS, Huang SM (2019) An experimental heuristic approach to multi-pose pedestrian dead reckoning without using magnetometers for indoor localization. IEEE Sensors J 19(20):9532–9542

    Article  Google Scholar 

  16. Lee MS, Ju H, Park CG (2017) Map assisted PDR/Wi-Fi fusion for indoor positioning using smartphone. Int J Control Autom Syst 15(2):627–639

    Article  Google Scholar 

  17. Leonardo R, Rodrigues G, Barandas M, Alves P, Santos R, Gamboa H (2019) Determination of the walking direction of a pedestrian from acceleration data. In: Proceedings of the International Conference on Indoor Positioning and Indoor Navigation, IPIN

    Google Scholar 

  18. Liu C, Pei L, Qian J, Wang L, Liu P, Yu W (2015) Sequence-based motion recognition assisted pedestrian dead reckoning using a smartphone. In: Proceedings of the China Satellite Navigation Conference, CSNC, pp 741–751

    Google Scholar 

  19. Li X, Wei D, Lai Q, Xu Y, Yuan H (2017) Smartphone-based integrated PDR/GPS/Bluetooth pedestrian location. Adv Space Res 59(3):877–887

    Article  Google Scholar 

  20. Li W, Chen R, Yu Y, Wu Y, Zhou H (2021) Pedestrian dead reckoning with novel heading estimation under magnetic interference and multiple smartphone postures. Measurement 182:109610

    Article  Google Scholar 

  21. Loh D, Student Member, Zihajehzadeh S, Student Member, Hoskinson R, Abdollahi H, Park EJ, Senior Member (2016) Pedestrian dead reckoning with smartglasses and smartwatch. IEEE Sensors J 16(22):8132–8141

    Article  Google Scholar 

  22. Manos A, Hazan T, Klein I (2022) Walking direction estimation using smartphone sensors: a deep network-based framework. IEEE Trans Instrum Meas 71:2501112

    Article  Google Scholar 

  23. Martínez del Horno M, Orozco-Barbosa L, García-Varea I (2021) A smartphone-based multimodal indoor tracking system. Inf Fusion 76:36–45

    Article  Google Scholar 

  24. Nabil M, Abdelhalim MB, AbdelRaouf A (2018) Enhancing indoor localization using IoT techniques. Adv Intell Syst Comput 639:885–894

    Google Scholar 

  25. Nowicki M, Skrzypczyński P (2015) Indoor navigation with a smartphone fusing inertial and WiFi data via factor graph optimization. In: Proceedings of the International Conference on Mobile Computing, Applications, and Services, MobiCASE, pp 280–298

    Google Scholar 

  26. Orr RJ, Abowd GD (2000) The smart floor : a mechanism for natural user identification and tracking. In: Proceedings of the ACM Conference Human Factors in Computing Systems, CHI, pp 275–276

    Google Scholar 

  27. Park SY, Heo SJ, Park CG (2017) Accelerometer-based smartphone step detection using machine learning technique. In: Proceedings of the 2017 IEEE International Electrical Engineering Congress, iEECON, pp 1–5

    Google Scholar 

  28. Pham TT, Suh YS (2021) Walking step length estimation using waist-mounted inertial sensors with known total walking distance. IEEE Access 9:85476–85487

    Article  Google Scholar 

  29. Priyantha NB, Chakraborty A, Balakrishnan H (2000) The cricket location-support system. In: Proceedings of the ACM Annual International Conference on Mobile Computing and Networking, MobiCom, pp 32–43

    Google Scholar 

  30. Racko J, Brida P, Perttula A, Parviainen J, Collin J (2016) Pedestrian dead reckoning with particle filter for handheld smartphone. In: Proceedings of the International Conference on Indoor Positioning and Indoor Navigation, IPIN, pp 4–7

    Google Scholar 

  31. Sato D, Togawa N (2022) A PDR method using smartglasses reducing accumulated errors by detecting user’s stop motions. In: Proceedings of the International Conference on Consumer Electronics, ICCE, pp 1–2

    Google Scholar 

  32. Skyhook Wireless, Inc., https://www.skyhook.com/

  33. Suh YS, Nemati E, Sarrafzadeh M (2016) Kalman-filter-based walking distance estimation for a smart-watch. In: Proceedings of the IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE, pp 150–156

    Google Scholar 

  34. Teng J, Zhang B, Zhu J, Li X, Xuan D, Zheng YF (2014) EV-loc: integrating electronic and visual signals for accurate localization. IEEE/ACM Trans Netw 22(4):1285–1296

    Article  Google Scholar 

  35. Tian Z, Zhang Y, Zhou M, Liu Y (2014) Pedestrian dead reckoning for MARG navigation using a smartphone. Eurasip J Adv Signal Process 2014:65

    Article  Google Scholar 

  36. Tian Q, Salcic Z, Kai Wang KI, Pan Y (2016) A multi-mode dead reckoning system for pedestrian tracking using smartphones. IEEE Sensors J 16(7):2079–2093

    Article  Google Scholar 

  37. Tiglao NM, Alipio M, Cruz RD, Bokhari F, Rauf S, Khan SA (2021) Smartphone-based indoor localization techniques : state-of-the-art and classification. Measurement 179:109349

    Article  Google Scholar 

  38. Uddin M, Gupta A, Maly K, Nadeem T, Godambe S, Zaritsky A (2014) SmartSpaghetti: accurate and robust tracking of Human’s location. In: Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI, pp 129–132

    Google Scholar 

  39. Wakaizumi T, Togawa N (2021) An indoor positioning method using smartphone and smartwatch independent of carrying modes. In: Proceedings of the IEEE International Conference on Consumer Electronics, ICCE

    Google Scholar 

  40. Wakaizumi T, Togawa N (2022) Carrying-mode free indoor positioning using smartphone and smartwatch and its evaluations. J Inf Process 30:52–65

    Google Scholar 

  41. Wang A, Ou X, Wang B (2019) Improved step detection and step length estimation based on pedstrian dead reckoning. In: Proceedings of the IEEE International Symposium on Electromagnetic Compatibility, ISEMC, pp 1–4

    Google Scholar 

  42. Weinberg H (2002) Using the ADXL202 in pedometer and personal navigation applications. In: Analog devices. Norwood, MA

    Google Scholar 

  43. Wu Y, Zhu H, Du Q, Tang S (2019) A pedestrian dead-reckoning system for walking and marking time mixed movement using an SHSs scheme and a foot-mounted IMU. IEEE Sensors J 19(5):1661–1671

    Article  Google Scholar 

  44. Wu Y, Zhu HB, Du QX, Tang SM (2019) A survey of the research status of pedestrian dead reckoning systems based on inertial sensors. Int J Autom Comput 16(1):65–83

    Article  Google Scholar 

  45. Xiao Z, Wen H, Markham A, Trigoni N (2014) Robust pedestrian dead reckoning (R-PDR) for arbitrary mobile device placement. In: Proceedings of the International Conference on Indoor Positioning and Indoor Navigation, IPIN, pp 187–196

    Google Scholar 

  46. Xu L, Xiong Z, Liu J, Wang Z, Ding Y (2019) A novel pedestrian dead reckoning algorithm for multi-mode recognition based on smartphones. Remote Sens 11(3):294

    Article  Google Scholar 

  47. Yao H, Shu H, Sun H, Mousa BG, Jiao Z, Suo Y (2020) An integrity monitoring algorithm for WiFi/PDR/smartphone-integrated indoor positioning system based on unscented Kalman filter. Eurasip J Wirel Commun Netw 2020(1):246

    Article  Google Scholar 

  48. Yu N, Zhan X, Zhao S, Wu Y, Feng R (2018) A precise dead reckoning algorithm based on bluetooth and multiple sensors. IEEE Internet Things J 5(1):336–351

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the editors and Dai Sato from Waseda University for their detailed and insightful comments, which greatly improved this chapter. This work was supported in part by JST CREST Grant Number JPMJCR19K4, and Japan. S. Bao was partially supported by JSPS Grant-in-Aid for Young Scientists (Grant No. 21K17747).

Author information

Authors and Affiliations

  1. Deptartemnt of Computer Science and Communications Engineering, Waseda University, Tokyo, Japan

    Siya Bao & Nozomu Togawa

Authors
  1. Siya Bao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nozomu Togawa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Siya Bao .

Editor information

Editors and Affiliations

  1. Colorado State University, Fort Collins, CO, USA

    Saideep Tiku

  2. Colorado State University, Fort Collins, CO, USA

    Sudeep Pasricha

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Bao, S., Togawa, N. (2023). Smart Device-Based PDR Methods for Indoor Localization. In: Tiku, S., Pasricha, S. (eds) Machine Learning for Indoor Localization and Navigation. Springer, Cham. https://doi.org/10.1007/978-3-031-26712-3_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-26712-3_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26711-6

  • Online ISBN: 978-3-031-26712-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation