iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://doi.org/10.1007/978-3-030-58820-5_54
Biomechanics Sensor Node for Virtual Reality: A Wearable Device Applied to Gait Recovery for Neurofunctional Rehabilitation | SpringerLink
Skip to main content
Springer Nature Link
Log in

Biomechanics Sensor Node for Virtual Reality: A Wearable Device Applied to Gait Recovery for Neurofunctional Rehabilitation

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12255))

Included in the following conference series:

Abstract

In several segments of the health areas, sensing has become a trend. Sensors allow data quantification for use in decision making or even to predict the clinical evolution of a given treatment, such as in rehabilitation therapies to restore patients’ motor and cognitive functions. This paper presents the Biomechanics Sensor Node (BSN), composed of an inertial measurement unit (IMU), developed to infer input information and control virtual environments. We also present a software solution, which integrates the BSN data with Unity Editor, one of the most used game engine nowadays. This asset allows Unity-developed virtual reality applications to use BSN a secure interaction device. Thus, during rehabilitation sessions, the patient receives visual stimuli from the virtual environment, controlled by the BSN device, while the therapist has access to the information about the movements performed in the therapy.

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

Access this chapter

Log in via an institution

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
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Similar content being viewed by others

Notes

  1. 1.

    The Asset is an item (e.g., source code, a 3D model, an audio file or an image) that facilitates to create Unity applications. An asset can be used to build diverse applications. Unity is a cross-platform engine that is used to develop games on multiple platforms.

References

  1. Aşkın, A., Atar, E., Koçyiğit, H., Tosun, A.: Effects of kinect-based virtual reality game training on upper extremity motor recovery in chronic stroke. Somatosens. Mot. Res. 35(1), 25–32 (2018)

    Article  Google Scholar 

  2. Beltrame, T., Amelard, R., Wong, A., Hughson, R.L.: Extracting aerobic system dynamics during unsupervised activities of daily living using wearable sensor machine learning models. J. Appl. Physiol. 124(2), 473–481 (2017)

    Article  Google Scholar 

  3. Bernhard, F.P., Sartor, J., Bettecken, K., Hobert, M.A., Arnold, C., Weber, Y.G., Poli, S., Margraf, N.G., Schlenstedt, C., Hansen, C., et al.: Wearables for gait and balance assessment in the neurological ward-study design and first results of a prospective cross-sectional feasibility study with 384 inpatients. BMC Neurol. 18(1), 114 (2018)

    Article  Google Scholar 

  4. Brandao, A.F., Dias, D.R., Castellano, G., Parizotto, N.A., Trevelin, L.C.: Rehabgesture: an alternative tool for measuring human movement. Telemedicine e-Health 22(7), 584–589 (2016)

    Article  Google Scholar 

  5. Brandão, A.F., Dias, D.R.C., Guimarães, M.P., Trevelin, L.C., Parizotto, N.A., Castellano, G.: Gesturecollection for motor and cognitive stimuli: virtual reality and e-health prospects. J. Health Inform. 10(1), 9–16 (2018)

    Google Scholar 

  6. Byrom, B., McCarthy, M., Schueler, P., Muehlhausen, W.: Brain monitoring devices in neuroscience clinical research: the potential of remote monitoring using sensors, wearables, and mobile devices. Clin. Pharmacol. Ther. 104, 59–71 (2018)

    Article  Google Scholar 

  7. Cameirao, M.S., i Badia, S.B., Duarte, E., Frisoli, A., Verschure, P.F.: The combined impact of virtual reality neurorehabilitation and its interfaces on upper extremity functional recovery in patients with chronic stroke. Stroke 43(10), 2720–2728 (2012)

    Article  Google Scholar 

  8. Cushman, L.A., Stein, K., Duffy, C.J.: Detecting navigational deficits in cognitive aging and alzheimer disease using virtual reality. Neurology 71(12), 888–895 (2008)

    Article  Google Scholar 

  9. Dias, D.R.C., Alvarenga, I.C., Guimarães, M.P., Trevelin, L.C., Castellano, G., Brandão, A.F.: eStreet: virtual reality and wearable devices applied to rehabilitation. In: Gervasi, O., Murgante, B., Misra, S., Stankova, E., Torre, C.M., Rocha, A.M.A.C., Taniar, D., Apduhan, B.O., Tarantino, E., Ryu, Y. (eds.) ICCSA 2018. LNCS, vol. 10963, pp. 775–789. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95171-3_60

    Chapter  Google Scholar 

  10. dojot: dojot documentation (2019). https://dojotdocs.readthedocs.io/en/latest/. Accessed Dec 2019

  11. Foxlin, E.: Pedestrian tracking with shoe-mounted inertial sensors. IEEE Comput. Graph. Appl. 6, 38–46 (2005)

    Article  Google Scholar 

  12. Frade, M.C., dos Reis, I.M., Basso-Vanelli, R.P., Brandão, A.F., Jamami, M.: Reproducibility and validity of the 6-minute stationary walk test associated with virtual reality in subjects with COPD. Respiratory care, pp. respcare-06237 (2019). https://doi.org/10.4187/respcare.06237

  13. Hadjidj, A., Bouabdallah, A., Challal, Y.: Rehabilitation supervision using wireless sensor networks. In: 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 1–3. IEEE (2011)

    Google Scholar 

  14. Hsu, Y.L., Yang, S.C., Chang, H.C., Lai, H.C.: Human daily and sport activity recognition using a wearable inertial sensor network. IEEE Access 6, 31715–31728 (2018)

    Article  Google Scholar 

  15. Laver, K.E., George, S., Thomas, S., Deutsch, J.E., Crotty, M.: Virtual reality for stroke rehabilitation. Cochrane Database Syst. Rev. (2) (2015)

    Google Scholar 

  16. Lloréns, R., Noé, E., Colomer, C., Alcañiz, M.: Effectiveness, usability, and cost-benefit of a virtual reality-based telerehabilitation program for balance recovery after stroke: a randomized controlled trial. Arch. Phys. Med. Rehabil. 96(3), 418–425 (2015)

    Article  Google Scholar 

  17. Nichols-Larsen, D.S., Clark, P., Zeringue, A., Greenspan, A., Blanton, S.: Factors influencing stroke survivors’ quality of life during subacute recovery. Stroke 36(7), 1480–1484 (2005)

    Article  Google Scholar 

  18. Piron, L., et al.: Exercises for paretic upper limb after stroke: a combined virtual-reality and telemedicine approach. J. Rehabil. Med. 41(12), 1016–1020 (2009)

    Article  Google Scholar 

  19. Rose, F.D., Brooks, B.M., Rizzo, A.A.: Virtual reality in brain damage rehabilitation. Cyberpsychol. Behav. 8(3), 241–262 (2005)

    Article  Google Scholar 

  20. dos Santos Mendes, F.A., et al.: Motor learning, retention and transfer after virtual-reality-based training in parkinson’s disease-effect of motor and cognitive demands of games: a longitudinal, controlled clinical study. Physiotherapy 98(3), 217–223 (2012)

    Article  Google Scholar 

  21. Saposnik, G., et al.: Efficacy and safety of non-immersive virtual reality exercising in stroke rehabilitation (EVREST): a randomised, multicentre, single-blind, controlled trial. Lancet Neurol. 15(10), 1019–1027 (2016)

    Article  Google Scholar 

  22. Schuster-Amft, C., et al.: Effect of a four-week virtual reality-based training versus conventional therapy on upper limb motor function after stroke: a multicenter parallel group randomized trial. PLoS ONE 13(10), e0204455 (2018)

    Article  Google Scholar 

  23. Shin, J.H., Ryu, H., Jang, S.H.: A task-specific interactive game-based virtual reality rehabilitation system for patients with stroke: a usability test and two clinical experiments. J. Neuroeng. Rehab. 11(1), 32 (2014)

    Article  Google Scholar 

  24. Statements, A.S.: Heart disease and stroke statistics 2013 update. Circulation 127(1), e6 (2013)

    Google Scholar 

  25. Steuer, J.: Defining virtual reality: dimensions determining telepresence. J. Commun. 42(4), 73–93 (1992)

    Article  Google Scholar 

  26. SwordHealth: Reinventing Physical Threapy (2019). https://swordhealth.com/. Accessed Feb 2019

  27. Tieri, G., Morone, G., Paolucci, S., Iosa, M.: Virtual reality in cognitive and motor rehabilitation: facts, fiction and fallacies. Expert Rev. Med. Dev. 15(2), 107–117 (2018)

    Article  Google Scholar 

  28. Tobler-Ammann, B.C., et al.: Exergames encouraging exploration of hemineglected space in stroke patients with visuospatial neglect: a feasibility study. JMIR Serious Games 5(3), e17 (2017)

    Article  Google Scholar 

  29. Tregillus, S., Folmer, E.: VR-step: walking-in-place using inertial sensing for hands free navigation in mobile VR environments. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp. 1250–1255. ACM (2016)

    Google Scholar 

  30. Turolla, A., et al.: Virtual reality for the rehabilitation of the upper limb motor function after stroke: a prospective controlled trial. J. Neuroeng. Rehab. 10(1), 85 (2013)

    Article  Google Scholar 

  31. Vanbellingen, T., Filius, S.J., Nyffeler, T., van Wegen, E.E.: Usability of videogame-based dexterity training in the early rehabilitation phase of stroke patients: a pilot study. Front. Neurol. 8, 654 (2017)

    Article  Google Scholar 

  32. Wade, E., Winstein, C.J.: Virtual reality and robotics for stroke rehabilitation: where do we go from here? Top. Stroke Rehab. 18(6), 685–700 (2011)

    Article  Google Scholar 

  33. Wardini, R., et al.: Using a virtual game system to innovate pulmonary rehabilitation: safety, adherence and enjoyment in severe chronic obstructive pulmonary disease. Can. Respir. J. 20(5), 357–361 (2013)

    Article  Google Scholar 

  34. Werium: INICIO - Werium Solutions (2019). http://www.weriumsolutions.com/. Accessed Feb 2019

  35. Wittmann, F., et al.: Self-directed arm therapy at home after stroke with a sensor-based virtual reality training system. J. Neuroeng. Rehab. 13(1), 75 (2016)

    Article  Google Scholar 

  36. Zago, M., et al.: Gait evaluation using inertial measurement units in subjects with parkinson’s disease. J. Electromyogr. Kinesiol. (2018)

    Google Scholar 

  37. Zhang, Z.: Microsoft kinect sensor and its effect. IEEE Multimed. 19(2), 4–10 (2012)

    Article  Google Scholar 

  38. Zhou, H., Razjouyan, J., Halder, D., Naik, A.D., Kunik, M.E., Najafi, B.: Instrumented trail-making task: application of wearable sensor to determine physical frailty phenotypes. Gerontology 65, 186–197 (2018)

    Article  Google Scholar 

Download references

Funding

FAPESP (Sao Paulo Research Foundation, Brazil) grant number 2015/03695-5 (grant related to author: A.F.B.). The EMBRAPII (Brazilian Agency for Research and Industrial Innovation) and SEBRAE made a financial contribution to the development of BSN.

Author information

Authors and Affiliations

  1. Institute of Physics Gleb Wataghin, University of Campinas, Campinas, SP, Brazil

    Alexandre Fonseca Brandão & Gabriela Castellano

  2. Brazilian Institute of Neuroscience and Neurotechnology, University of Campinas, Campinas, SP, 13083887, Brazil

    Alexandre Fonseca Brandão, Diego Roberto Colombo Dias, Thomas Beltrame & Gabriela Castellano

  3. Institute for Bioengineering of Catalonia - IBEC, Barcelona, Spain

    Alexandre Fonseca Brandão

  4. Universidade Federal de São João del-Rei, São João del-Rei, MG, Brazil

    Diego Roberto Colombo Dias & Sávyo Toledo Machado Reis

  5. Universidade Federal de São Paulo, São Paulo, SP, Brazil

    Maria Cecilia Moraes Frade, Thomas Beltrame & Marcelo de Paiva Guimarães

  6. Centro Universitário Campo Limpo Paulista - UNIFACCAMP, Campo Limpo Paulista, Brazil

    Marcelo de Paiva Guimarães

  7. Center for Research and Development in Telecommunications (CPqD), Campinas, SP, Brazil

    Clovis Magri Cabreira

Authors
  1. Alexandre Fonseca Brandão
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Diego Roberto Colombo Dias
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sávyo Toledo Machado Reis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Clovis Magri Cabreira
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Maria Cecilia Moraes Frade
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Thomas Beltrame
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Marcelo de Paiva Guimarães
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Gabriela Castellano
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Diego Roberto Colombo Dias .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Brandão, A.F. et al. (2020). Biomechanics Sensor Node for Virtual Reality: A Wearable Device Applied to Gait Recovery for Neurofunctional Rehabilitation. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12255. Springer, Cham. https://doi.org/10.1007/978-3-030-58820-5_54

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58820-5_54

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58819-9

  • Online ISBN: 978-3-030-58820-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation