Making Pier Data Broader and Deeper:

PDR Challenge and Virtual Mapping Party
  • Takeshi KurataEmail author
  • Ryosuke Ichikari
  • Ryo Shimomura
  • Katsuhiko Kaji
  • Takashi Okuma
  • Masakatsu Kourogi
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 240)


Big data can be gathered on a daily basis, but it has issues on its quality and variety. On the other hand, deep data is obtained in some special conditions such as in a lab or in a field with edge-heavy devices. It compensates for the above issues of big data, and also it can be training data for machine learning. Just like a platform of pier supported by stakes, there is structure in which big data is supported by deep data. That is why we call the combination of big and deep data “pier data.” By making pier data broader and deeper, it becomes much easier to understand what is happening in the real world and also to realize Kaizen and innovation. We introduce two examples of activities on making pier data broader and deeper. First, we outline “PDR Challenge in Warehouse Picking”; a PDR (Pedestrian Dead Reckoning) performance competition which is very useful for gathering big data on behavior. Next, we discuss methodologies of how to gather and utilize pier data in “Virtual Mapping Party” which realizes map-content creation at any time and from anywhere to support navigation services for visually impaired individuals.


Lab-forming fields Field-forming labs Big data Deep data Pier data PDR IoT IoH VR Service engineering 



The PDR Challenge in Warehouse Picking was carried out partially with the support of JST OPERA’s “Elucidation of the Mechanism of Cooperation Between Humans and Intelligent Machines and the Creation of Fundamental Technology to Build a New Social System Based on Cooperative Value.” The virtual mapping party was carried out with the support of JST RISTEX’s “Development of a Movement Support System for Visually Impaired People by Multi-Generation Co-Creation.”


  1. 1.
    Kurata, T., Ichikari, R., Chang, C.-T., Kourogi, M., Onishi, M., Okuma, T.: Lab-forming fields and field-forming labs. Serviceology for Services. LNCS, vol. 10371, pp. 144–149. Springer, Cham (2017). Scholar
  2. 2.
    Kourogi, M., Kurata, T.: Personal positioning based on walking locomotion analysis with self-contained sensors and a wearable camera. In: Proceedings of the ISMAR 2003, pp. 103–112 (2003)Google Scholar
  3. 3.
    Kourogi, M., Kurata, T.: A method of pedestrian dead reckoning for smartphones using frequency domain analysis on patterns of acceleration and angular velocity. In: Proceedings of the PLANS 2014, pp. 164–168 (2014)Google Scholar
  4. 4.
    DoCoMo Map Indoor Navigation Area.
  5. 5.
    Harle, R.: A survey of indoor inertial positioning systems for pedestrians. IEEE Commun. Surv. Tutor. 15(3), 1281–1293 (2013)CrossRefGoogle Scholar
  6. 6.
    Foxlin, E.: Pedestrian tracking with shoe-mounted inertial sensors. IEEE Comput. Graph. Appl. 25(6), 38–46 (2005)CrossRefGoogle Scholar
  7. 7.
    Kaji, K., Kawaguchi, N.: 3D walking trajectory estimation method based on stable sensing section detection. Trans. Inf. Process. Soc. Jpn 57(1), 12–24 (2016)Google Scholar
  8. 8.
    Kaji, K., Kawaguchi, N.: Estimating 3D pedestrian trajectories using stability of sensing signal. In: Proceedings of the Seventh International Conference on Indoor Positioning and Indoor Navigation (IPIN2016) (2016)Google Scholar
  9. 9.
    Combettes, C., Renaudin, V.: Comparison of misalignment estimation techniques between handheld device and walking directions. In: Proceedings of the IPIN 2015, 8 p. (2015)Google Scholar
  10. 10.
    Ichikari, R., Ruiz, L.C.M., Kourogi, M., Kitagawa, T., Yoshii, S., Kurata, T.: Indoor floor-level detection by collectively decomposing factors of atmospheric pressure. In: Proceedings of the IPIN 2015, 11 p. (2015)Google Scholar
  11. 11.
    Makita, K., Kourogi, M., Ishikawa, T., Okuma, T., Kurata, T.: PDR plus: human behaviour sensing method for service field analysis. In: Proceedings of the ICServ 2013, pp. 19–22 (2013)Google Scholar
  12. 12.
    Kanagu, K., Tsubouchi, K., Nishio, N.: Colorful PDR: Colorizing PDR with shopping context in walking. In: Proceedings of the IPIN 2017 (2017)Google Scholar
  13. 13.
    Kaji, K., Abe, M., Wang, W., Hiroi, K., Kawaguchi, N.: UbiComp/ISWC 2015 PDR challenge corpus. In Proceedings of the HASCA2016 (UbiComp2016 Proceedings: Adjunct), pp. 696–704 (2016)Google Scholar
  14. 14.
    Kaji, K., Kohei Kanagu, K., Murao, K., Nishio, N., Urano, K., Iida, H., Kawaguchi, N.: Multi-algorithm on-site evaluation system for PDR challenge. In: Proceedings of the Ninth International Conference on Mobile Computing and Ubiquitous Networking (ICMU2016), pp. 1–6 (2016)Google Scholar
  15. 15.
    Lymberopoulos, D., Liu, J.: The microsoft indoor localization competition: experiences and lessons learned. IEEE Signal Process. Mag. 34(5), 125–140 (2017)CrossRefGoogle Scholar
  16. 16.
  17. 17.
    PDR Benchmark Standardization Committee.
  18. 18.
    PDR Challenge in Warehouse Picking.
  19. 19.
    Frameworx Logistics Open Data Contest.
  20. 20.
    Abe, M., Kaji, K., Hiroi, K., Kawaguchi, N.: PIEM: path independent evaluation metric for relative localization. In: Proceedings of the Seventh International Conference on Indoor Positioning and Indoor Navigation (IPIN2016) (2016)Google Scholar
  21. 21.
    Ishikawa, T., Kourogi, M., Kurata, T.: Economic and synergistic pedestrian tracking system with service cooperation for indoor environments. Int. J. Organ. Collective Intell. 2(1), 1–20 (2011)CrossRefGoogle Scholar
  22. 22.
    Ichikari, R., Kurata, T.: Virtual mapping party: co-creation of maps for visually impaired people. J. Technol. Persons with Disabil. 5, 208–224 (2017)Google Scholar
  23. 23.
    Kurata, T., Kourogi, M., Ishikawa, T., Kameda, Y., Aoki, K., Ishikawa, J.: Indoor-outdoor navigation system for visually-impaired pedestrians: preliminary evaluation of position measurement and obstacle display. In: ISWC 2011, pp. 123–124 (2011)Google Scholar
  24. 24.
    Kurata, T., Seki, Y., Kourogi, M., Ishikawa, J.: Roles of navigation system in walking with long cane and guide dog. In: The 29th Annual International Technology and Persons with Disabilities Conference (CSUN 2014) (2014)Google Scholar
  25. 25.
    Denoncin, S.: AX’S: A New Indoor GPS Solution Designed for All, BLV-034, CSUN 2014 (2014)Google Scholar
  26. 26.
    Ichikari, R., Yanagimachi, T., Kurata, T.: Augmented reality tactile map with hand gesture recognition. In: Miesenberger, K., Bühler, C., Penaz, P. (eds.) ICCHP 2016. LNCS, vol. 9759, pp. 123–130. Springer, Cham (2016). Scholar
  27. 27.
    Miura, T., Yabu, K.-i., Noro, T., Segawa, T., Kataoka, K., Nishimuta, A., Sanmonji, M., Hiyama, A., Hirose, M., Ifukube, T.: Sharing Real-World Accessibility Conditions Using a Smartphone Application by a Volunteer Group. In: Miesenberger, K., Bühler, C., Penaz, P. (eds.) ICCHP 2016. LNCS, vol. 9759, pp. 265–272. Springer, Cham (2016). Scholar
  28. 28.
    Hara, K., Azenkot, S., Campbell, M., Bennett, C.L., Le, V., Pannella, S., Moore, R., Minckler, K., Ng, R.H., Froehlich, J.E.: Improving public transit accessibility for blind riders by crowdsourcing bus stop landmark locations with google street view: an extended analysis. ACM Trans. Access. Comput. (TACCESS) 6(2), 23 (2015). Article 5Google Scholar
  29. 29.
  30. 30.
  31. 31.
    Voigt, C., Dobner, S., Ferri, M., Hahmann, S., Gareis, K.: Community engagement strategies for crowdsourcing accessibility information - Paper, Wheelmap-Tags and Mapillary-Walks. In: Proceedings of the ICCHP (2), pp. 257–264 (2016)Google Scholar
  32. 32.
    Kourogi, M., Kurata, T.: Vibration-based vehicle dead reckoning (VDR) for localization of wheeled vehicles. In: IEEE/ION PLANS 2018 Conference (2018, accepted)Google Scholar
  33. 33.
    Ichikari, R., Chang, C.-T., Michitsuji, K., Kitagawa, T., Yoshii, S., Kurata, T.: Complementary integration of PDR with absolute positioning methods based on time-series consistency. In: Proceedings of the IPIN 2016, 195_WIP (2016)Google Scholar
  34. 34.
    Fukuhara, T., Tenmoku, R., Ueoka, R., Okuma, T., Kurata, T.: Estimating skills of waiting staff of a restaurant based on behavior sensing and POS data analysis: a case study in a Japanese cuisine restaurant. In: Proceedings of the AH-FE2014, pp. 4287–4299 (2014)Google Scholar
  35. 35.
    Myokan, T., Matsumoto, M., Okuma, T., Ichikari, R., Kato, K., Ota, D., Kurata, T.: Pre-evaluation of Kaizen plan considering efficiency and employee satisfaction by simulation using data assimilation-Toward constructing Kaizen support framework.In: Proceedings of the ICServ 2016, 7 p. (2016)Google Scholar
  36. 36.
    Benchmarking of vison-based spatial registration and tracking methods for Mixed and Augmented Reality (MAR), ISO/IEC 18520Google Scholar
  37. 37.
    Ahmetovic, D., Gleason, C., Ruan, C., Kitani, K., Takagi, H., Asakawa, C.: NavCog: a navigational cognitive assistant for the blind. In: Proceedings of the MobileHCI 2016, pp. 90–99 (2016)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Takeshi Kurata
    • 1
    • 2
    Email author
  • Ryosuke Ichikari
    • 1
  • Ryo Shimomura
    • 1
    • 2
  • Katsuhiko Kaji
    • 3
  • Takashi Okuma
    • 1
  • Masakatsu Kourogi
    • 1
    • 4
  1. 1.National Institute of Advanced Industrial Science and TechnologyTokyoJapan
  2. 2.Tsukuba UniversityTsukubaJapan
  3. 3.Aichi Institute of TechnologyToyotaJapan
  4. 4.Sitesensing Co. Ltd.TokyoJapan

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