Skip to main content

Barycentric Shift Model Based VR Application for Detection and Classification on Body Balance Disorders

  • Conference paper
  • First Online:
E-Learning and Games (Edutainment 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11462))

Included in the following conference series:

  • 1328 Accesses

Abstract

Virtual reality technology shows serious potential in many fields, such as cinemtic entertainment, professional training, Healthcare and clinical therapies, etc. In this paper, we propose a novel human balance capability evaluation method, which is based on crossing bridge virtual scene and video analysis. We have sampled the crossing bridge movement video of two groups of volunteers with balance ability differences, and then we proposed a balance ability classification algorithm via barycentric shifts model statistical analysis. The small sample experiment shows that our method can accurately identify the possible candidates with balance ability abnormality.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Ustinova, K.I., Leonard, W.A., Cassavaugh, N.D., et al.: Development of a 3D immersive videogame to improve arm-postural coordination in patients with TBI. J. Neuroeng. Rehabil. 8(1), 1–11 (2011)

    Article  Google Scholar 

  2. Rubin, M.A.: Make precision medicine work for cancer care: to get targeted treatments to more cancer patients pair genomic data with clinical data, and make the information widely accessible. Nature 520(7547), 290–292 (2015)

    Article  Google Scholar 

  3. Yin, C., Hsueh, Y.H., Yeh, C.Y., et al.: A virtual reality-cycling training system for lower limb balance improvement. Biomed. Res. Int. 2016(1), 1–10 (2016)

    Google Scholar 

  4. Liau, B.-Y., Lung, C.-W., Jan, Y.-K.: Development of human balance assessment system with continuous center of gravity tracking. In: Duffy, V.G. (ed.) DHM 2013. LNCS, vol. 8025, pp. 332–337. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39173-6_39

    Chapter  Google Scholar 

  5. Lafond, D., Duarte, M.F.: Comparison of three methods to estimate the center of mass during balance assessment. J. Biomech. 37(9), 1421–1426 (2004)

    Article  Google Scholar 

  6. Moraru, C., Neculaeş, M., Hodorcă, R.M.: Comparative study on the balance ability in sporty and unsporty children. Procedia - Soc. Behav. Sci. 116, 3659–3663 (2014)

    Article  Google Scholar 

  7. Lloréns, R., Gilgómez, J.A., Alcañiz, M., et al.: Improvement in balance using a virtual reality-based stepping exercise: a randomized controlled trial involving individuals with chronic stroke. Clin. Rehabil. 29(3), 261–268 (2015)

    Article  Google Scholar 

  8. Ferdous, S.M.S.: Improve accessibility of virtual and augmented reality for people with balance impairments. In: 2017 IEEE Virtual Reality, pp. 421–422 (2017)

    Google Scholar 

  9. Zhang, B.F., Zhou, J., Zhu, J.C.: Research on three image difference algorithm. In: International Conference on Image Analysis and Signal Processing, pp. 603–606. IEEE (2010)

    Google Scholar 

  10. Palaniappan, S.: Image denoising using median filter with edge detection using canny operator. Int. J. Sci. Res. 3(2), 30–34 (2014)

    Google Scholar 

  11. Lin, C.Y., Chai, H.C., Wang, J.Y., et al.: Augmented reality in educational activities for children with disabilities. Displays 42, 51–54 (2015)

    Article  Google Scholar 

  12. Lakhani, B., Mansfield, A.: Visual feedback of the centre of gravity to optimize standing balance. Gait Posture 41(2), 499–503 (2015)

    Article  Google Scholar 

  13. Tarabalka, Y., Fauvel, M., Chanussot, J., et al.: SVM- and MRF-based method for accurate classification of hyperspectral images. IEEE Geosci. Remote Sens. Lett. 7(4), 736–740 (2010)

    Article  Google Scholar 

  14. Ayyaz, M.N., Javed, I., Mahmood, W.: Handwritten character recognition using multiclass SVM classification with hybrid feature extraction. Pak. J. Eng. Appl. Sci. 10, 57–67 (2016)

    Google Scholar 

Download references

Acknowledgment

This work is supported in part by the National Natural Science Foundation of China under grant Nos. 61472204, 6150238.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Haiyan Jin .

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

Jin, H., Lin, W., Xiao, Z., Liu, H., Wang, B., Li, X. (2019). Barycentric Shift Model Based VR Application for Detection and Classification on Body Balance Disorders. In: El Rhalibi, A., Pan, Z., Jin, H., Ding, D., Navarro-Newball, A., Wang, Y. (eds) E-Learning and Games. Edutainment 2018. Lecture Notes in Computer Science(), vol 11462. Springer, Cham. https://doi.org/10.1007/978-3-030-23712-7_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-23712-7_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23711-0

  • Online ISBN: 978-3-030-23712-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics