Skip to main content

VR-Fit: Walking-in-Place Locomotion with Real Time Step Detection for VR-Enabled Exercise

  • Conference paper
  • First Online:
Mobile Web and Intelligent Information Systems (MobiWIS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11673))

Abstract

With recent advances in mobile and wearable technologies, virtual reality (VR) found many applications in daily use. Today, a mobile device can be converted into a low-cost immersive VR kit thanks to the availability of do-it-yourself viewers in the shape of simple cardboards and compatible software for 3D rendering. These applications involve interacting with stationary scenes or moving in between spaces within a VR environment. VR locomotion can be enabled through a variety of methods, such as head movement tracking, joystick-triggered motion and through mapping natural movements to translate to virtual locomotion. In this study, we implemented a walk-in-place (WIP) locomotion method for a VR-enabled exercise application. We investigate the utility of WIP for exercise purposes, and compare it with joystick-based locomotion in terms of step performance and subjective qualities of the activity, such as enjoyment, encouragement for exercise and ease of use. Our technique uses vertical accelerometer data to estimate steps taken during walking or running, and locomotes the user’s avatar accordingly in virtual space. We evaluated our technique in a controlled experimental study with 12 people. Results indicate that the way users control the simulated locomotion affects how they interact with the VR simulation, and influence the subjective sense of immersion and the perceived quality of the interaction. In particular, WIP encourages users to move further, and creates a more enjoyable and interesting experience in comparison to joystick-based navigation.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

References

  1. Google Cardboard. https://vr.google.com/cardboard/

  2. Mobile VR users worldwide 2015–2020—Statistic. http://www.statista.com/statistics/650834/mobile-vr-users-worldwide/. Accessed 11 Dec 2018

  3. Riecke, B.E., Bodenheimer, B., McNamara, T.P., Williams, B., Peng, P., Feuereissen, D.: Do we need to walk for effective virtual reality navigation? Physical rotations alone may suffice. In: Hölscher, C., Shipley, T.F., Olivetti Belardinelli, M., Bateman, J.A., Newcombe, N.S. (eds.) Spatial Cognition 2010. LNCS (LNAI), vol. 6222, pp. 234–247. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14749-4_21

    Chapter  Google Scholar 

  4. Darken, R.P., Cockayne, W.R., Carmein, D.: The omni-directional treadmill. In: Proceedings of the 10th Annual ACM Symposium on User Interface Software and Technology - UIST 1997 (1997)

    Google Scholar 

  5. Usoh, M., et al.: Walking \(>\) walking-in-place \(>\) flying, in virtual environments. In: Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques - SIGGRAPH 1999 (1999)

    Google Scholar 

  6. Péruch, P., Belingard, L., Thinus-Blanc, C.: Transfer of spatial knowledge from virtual to real environments. In: Freksa, C., Habel, C., Brauer, W., Wender, K.F. (eds.) Spatial Cognition II. LNCS (LNAI), vol. 1849, pp. 253–264. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45460-8_19

    Chapter  Google Scholar 

  7. Feasel, J., Whitton, M.C., Wendt, J.D.: LLCM-WIP: low-latency, continuous-motion walking-in-place. In: 2008 IEEE Symposium on 3D User Interfaces (2008)

    Google Scholar 

  8. Chon, J., Cha, H.: LifeMap: a smartphone-based context provider for location-based services. IEEE Pervasive Comput. 10, 58–67 (2011)

    Article  Google Scholar 

  9. Jang, H.-J., Kim, J., Hwang, D.-H.: Robust step detection method for pedestrian navigation systems. Electron. Lett. 43, 749 (2007)

    Article  Google Scholar 

  10. Mladenov, M., Mock, M.: A step counter service for Java-enabled devices using a built-in accelerometer. In: Proceedings of the 1st International Workshop on Context-Aware Middleware and Services affiliated with the 4th International Conference on Communication System Software and Middleware (COMSWARE 2009) - CAMS 2009 (2009)

    Google Scholar 

  11. Wang, H., Sen, S., Elgohary, A., Farid, M., Youssef, M., Choudhury, R.R.: No need to war-drive. In: Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services - MobiSys 2012 (2012)

    Google Scholar 

  12. Pan, M.-S., Lin, H.-W.: A step counting algorithm for smartphone users: design and implementation. IEEE Sens. J. 15, 2296–2305 (2015)

    Article  Google Scholar 

  13. Bebek, O., et al.: Personal navigation via shoe mounted inertial measurement units. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (2010)

    Google Scholar 

  14. Lo, C.-C., Chiu, C.-P., Tseng, Y.-C., Chang, S.-A., Kuo, L.-C.: A walking velocity update technique for pedestrian dead-reckoning applications. In: 2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications (2011)

    Google Scholar 

  15. Ojeda, L., Borenstein, J.: Non-GPS navigation with the personal dead-reckoning system. In: Unmanned Systems Technology IX (2007)

    Google Scholar 

  16. Alzantot, M., Youssef, M.: UPTIME: ubiquitous pedestrian tracking using mobile phones. In: 2012 IEEE Wireless Communications and Networking Conference (WCNC) (2012)

    Google Scholar 

  17. Hu, W.-Y., Lu, J.-L., Jiang, S., Shu, W., Wu, M.-Y.: WiBEST: a hybrid personal indoor positioning system. In: 2013 IEEE Wireless Communications and Networking Conference (WCNC) (2013)

    Google Scholar 

  18. Zhang, R., Bannoura, A., Hoflinger, F., Reindl, L.M., Schindelhauer, C.: Indoor localization using a smart phone. In: 2013 IEEE Sensors Applications Symposium Proceedings (2013)

    Google Scholar 

  19. Brajdic, A., Harle, R.: Walk detection and step counting on unconstrained smartphones. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp 2013 (2013)

    Google Scholar 

  20. Lee, H.-H., Choi, S., Lee, M.-J.: Step detection robust against the dynamics of smartphones. Sensors 15, 27230–27250 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sercan Sari .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sari, S., Kucukyilmaz, A. (2019). VR-Fit: Walking-in-Place Locomotion with Real Time Step Detection for VR-Enabled Exercise. In: Awan, I., Younas, M., Ãœnal, P., Aleksy, M. (eds) Mobile Web and Intelligent Information Systems. MobiWIS 2019. Lecture Notes in Computer Science(), vol 11673. Springer, Cham. https://doi.org/10.1007/978-3-030-27192-3_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-27192-3_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27191-6

  • Online ISBN: 978-3-030-27192-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics