Advertisement

StraightenUp: Implementation and Evaluation of a Spine Posture Wearable

  • Gabriela Cajamarca
  • Iyubanit Rodríguez
  • Valeria HerskovicEmail author
  • Mauricio Campos
Conference paper
  • 1.4k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10586)

Abstract

Human posture and activity levels are indicators for assessing health and quality of life. Maintaining improper posture for an extended period of time can lead to health issues, e.g. improper alignment of the vertebrae and accelerated degenerative disc. This, in turn, can be the cause of back pain, neurological deterioration, deformity, and cosmetic issues. Some wearable prototypes have been proposed for spine posture monitoring, however, there has not been enough consideration for the users’ experience with these devices, to understand which characteristics are central to acceptance and long-term use. This paper presents a prototype of a low-cost spine posture wearable, along with its preliminary evaluation, which aims both to confirm that the wearable can measure spine posture and to evaluate user experience with this device. The results show that the wearable was comfortable, causing a sensation of security, and that feedback to users would be needed to help improve posture. Further work is required to make sure the device is easy to put on and remove, and discreet enough to be worn in public.

Notes

Acknowledgements

This project was supported partially by CONICYT-PCHA/Doctorado Nacional/2014-63140077, CONICIT and MICIT Costa Rica PhD scholarship grant, Universidad de Costa Rica and CONICYT/FONDECYT No1150365 (Chile).

References

  1. 1.
    Lewis, J.S., Valentine, R.E.: Clinical measurement of the thoracic kyphosis: a study of the intra-rater reliability in subjects with and without shoulder pain. BMC Musculoskelet. Disord. 11(1), 39 (2010)CrossRefGoogle Scholar
  2. 2.
    Varshney, U.: Pervasive healthcare and wireless health monitoring. Mob. Netw. Appl. 12(2–3), 113–127 (2007)CrossRefGoogle Scholar
  3. 3.
    Gureje, O., Von Korff, M., Simon, G.E., Gater, R.: Persistent pain and well-being: a world health organization study in primary care. JAMA 280(2), 147–151 (1998)CrossRefGoogle Scholar
  4. 4.
    Ribeiro, D.C., Sole, G., Abbott, J.H., Milosavljevic, S.: The effectiveness of a lumbopelvic monitor and feedback device to change postural behavior: a feasibility randomized controlled trial. J. Orthop. Sports Phys. Ther. 44(9), 702–711 (2014). PMID: 25098195CrossRefGoogle Scholar
  5. 5.
    Farra, N., El-Sayed, B., Moacdieh, N., Hajj, H., Hajj, Z., Haidar, R.: A mobile sensing and imaging system for real-time monitoring of spine health. J. Med. Imaging Health Inform. 1(3), 238–245 (2011)CrossRefGoogle Scholar
  6. 6.
    Harms, H., Amft, O., Tröster, G., Roggen, D.: Smash: A distributed sensing and processing garment for the classification of upper body postures. In: Proceedings of the ICST 3rd International Conference on Body Area Networks, BodyNets 2008, pp. 22:1–22:8. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), Brussels, Belgium (2008)Google Scholar
  7. 7.
    Saggio, G., Sbernini, L.: New scenarios in human trunk posture measurements for clinical applications. In: 2011 IEEE International Symposium on Medical Measurements and Applications, pp. 13–17, May 2011Google Scholar
  8. 8.
    Zheng, Y., Wong, W.K., Guan, X., Trost, S.: Physical activity recognition from accelerometer data using a multi-scale ensemble method. In: Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2013, pp. 1575–1581. AAAI (2013)Google Scholar
  9. 9.
    Walsh, M., O’Flynn, B., O’Mathuna, C., Hickey, A., Kellett, J.: Correlating average cumulative movement and barthel index in acute elderly care. In: O’Grady, M.J., Vahdat-Nejad, H., Wolf, K.-H., Dragone, M., Ye, J., Röcker, C., O’Hare, G. (eds.) AmI 2013. CCIS, vol. 413, pp. 54–63. Springer, Cham (2013). doi: 10.1007/978-3-319-04406-4_7 CrossRefGoogle Scholar
  10. 10.
    Atallah, L., Lo, B., King, R., Yang, G.Z.: Sensor placement for activity detection using wearable accelerometers. In: 2010 International Conference on Body Sensor Networks, pp. 24–29, June 2010Google Scholar
  11. 11.
    Lyons, G., Culhane, K., Hilton, D., Grace, P., Lyons, D.: A description of an accelerometer-based mobility monitoring technique. Med. Eng. Phys. 27(6), 497–504 (2005)CrossRefGoogle Scholar
  12. 12.
    Lou, E., Lam, G.C., Hill, D.L., Wong, M.S.: Development of a smart garment to reduce kyphosis during daily living. Med. Biol. Eng. Comput. 50(11), 1147–1154 (2012)CrossRefGoogle Scholar
  13. 13.
    Peetoom, K.K.B., Lexis, M.A.S., Joore, M., Dirksen, C.D., Witte, L.P.D.: Literature review on monitoring technologies and their outcomes in independently living elderly people. Disabil. Rehab. Assist. Technol. 10(4), 271–294 (2015)CrossRefGoogle Scholar
  14. 14.
    Swan, M.: The quantified self: Fundamental disruption in big data science and biological discovery. Big Data 1(2), 85–99 (2013)CrossRefGoogle Scholar
  15. 15.
    Gao, Y., Li, H., Luo, Y.: An empirical study of wearable technology acceptance in healthcare. Ind. Manag. Data Syst. 115(9), 1704–1723 (2015)CrossRefGoogle Scholar
  16. 16.
    Moon, B.C., Chang, H.: Technology acceptance and adoption of innovative smartphone uses among hospital employees. Healthc. Inform. Res. 20(10), 304–312 (2014)CrossRefGoogle Scholar
  17. 17.
    Kim, S.H.: Moderating effects of job relevance and experience on mobile wireless technology acceptance: adoption of a smartphone by individuals. Inform. Manag. 45(6), 387–393 (2008)CrossRefGoogle Scholar
  18. 18.
    Isleifsdottir, J., Larusdottir, M.: Measuring the user experience of a task oriented software. In: Proceedings of the International Workshop on Meaningful Measures: Valid Useful User Experience Measurement, Reykjavik, Iceland, vol. 8, pp. 97–101, June 2008Google Scholar
  19. 19.
    Attrakdiff: Attrakdiff. http://www.attrakdiff.de. Accessed 30 Mar 2017
  20. 20.
    Consortium, R: Take control of your r code (2016). https://www.rstudio.com/products/rstudio/download/
  21. 21.
    Braun, V., Clarke, V.: Using thematic analysis in psychology. Qual. Res. Psychol. 3(2), 77101 (2006)CrossRefGoogle Scholar
  22. 22.
    Gjoreski, H., Lustrek, M., Gams, M.: Accelerometer placement for posture recognition and fall detection. In: 2011 Seventh International Conference on Intelligent Environments, pp. 47–54, July 2011Google Scholar
  23. 23.
    Bayat, A., Pomplun, M., Tran, D.A.: A study on human activity recognition using accelerometer data from smartphones. Procedia Comput. Sci. 34, 450–457 (2014)CrossRefGoogle Scholar
  24. 24.
    Wang, Q., Chen, W., Markopoulos, P.: Smart garment design for rehabilitation. In: Fardoun, H.M., R. Penichet, V.M., Alghazzawi, D.M. (eds.) REHAB 2014. CCIS, vol. 515, pp. 260–269. Springer, Heidelberg (2015). doi: 10.1007/978-3-662-48645-0_22 CrossRefGoogle Scholar
  25. 25.
    Beech, R., Roberts, D.: Assistive technology and older people. SCIE website - briefing paper, August 2008Google Scholar
  26. 26.
    Ribeiro, D.C., Milosavljevic, S., Abbott, J.H.: Effectiveness of a lumbopelvic monitor and feedback device to change postural behaviour: a protocol for the elf cluster randomised controlled trial. BMJ Open 7(1), e015568 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer SciencePontificia Universidad Católica de ChileSantiagoChile
  2. 2.School of MedicinePontificia Universidad Católica de ChileSantiagoChile

Personalised recommendations