Skip to main navigation menu Skip to main content Skip to site footer

Architecture proposal for a support system to upper limb telerehabilitation by capturing biomechanical signals

Abstract

This article proposes a system for Telerehabilitation of people with motor disorders of the upper limb, by making a literature review about works related with the provision of the physical therapy service with ICT’s use. Likewise, there is a brief description of the modules integrating the system: motion capture system based on inertial sensors and motion capture with camera, joint angle estimator was implemented through Kalman filter, IT app which registers the electronic medical record and finally, the active videogames module.

Keywords

upper limb rehabilitation, inertial sensors, telerehabilitation, Kalman filter, e-health

PDF HTML

References

  1. R. L. Bashshur, On the Definition and Evaluation of Telemedicine. Telemedicine Journal. vol. 1, pp. 19-30. 1995.
  2. T. Yokoishi, H. Hada, J. Mitsugi, O. Nakamura, J. Murai, “Bidirectional medication support system for medical staff and home care patients”. Proceedings in 5th International Symposium Medical Information & Communication Technology, (Montreux, Switzerland), pp. 147–151, IEEE, 2011.
  3. J. Martín-Moreno, M. Ruiz-Fernández, A. Soriano-Payá, V.J. Berenguer-Miralles, “Monitoring 3D movements for the rehabilitation of joints in physiotherapy”. Proceedings In 30th Annual International IEEE EMBS, (Vancouver, British Columbia, Canada), pp. 4836 – 4839, 2008.
  4. S. F. Foo, H. Zhuo, W. Phyo, M. Jayachandran, J. Biswas, Y. Siew and P. Yap, “Innovative platform for Tele-physiotherapy”. Proceedings In 10th International Conference E-health Networking, Applications and Services. (Singapore), pp. 59 – 65, 2008.
  5. F. Martínez, F. Gómez and E. Romero, “Análisis de vídeo para estimación del movimiento humano: una revisión”. Revista Med, Vol. 17 (1), pp. 953-106, 2009.
  6. Health Level Seven International. Concepts. Available: http://www.hl7.org/, [Accessed:10-Jun-2014].
  7. Digital Imaging and Communication in Medicine. Available: http://dicom.nema.org/, [Accessed:10-May-2014].
  8. M. R. Golomb, B.C. McDonald, S.J. Warden, J. Yonkman, A. J. Saykin, B. Shirley, et al. In-home virtual reality videogame telerehabilitation in adolescents with hemiplegic cerebral palsy. Archives of physical medicine and rehabilitation. Vol .91, pp. 1–8, 2010.
  9. D. L. Ines, G. Abdelkader. “Mixed reality serious games: The therapist perspective”, Proceedings in IEEE 1st International Conference on Serious Games and Applications for Health (Dublin, Ireland), pp. 1–10, 2011.
  10. G. C. Burdea, A. Jain, B. Rabin, R. Pellosie, and M. Golomb, “Long-term hand telerehabilitation on the PlayStation 3: benefits and challenges”. Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference. (Bosto, USA), pp. 1835– 1838, 2011.
  11. C. Franco, A. Fleury, P. Y. Gumery, B. Diot, J. Demongeot, N. Vuillerme. iBalance-ABF: a smartphone-based audio-biofeedback balance system, IEEE transactions on bio-medical engineering, vol. 60, pp. 211–215, 2013.
  12. I. C. Jeong, J. Finkelstein. Introducing a practical approach for non-invasive blood pressure monitoring during home-based telerehabilitation exercise program. IEEE Pointof- Care Healthcare Technologies, pp. 164–167, 2013.
  13. M. Piqueras, E. Marco, M. Coll, F. Escalada, A. Ballester, C. Cinca. Effectiveness of an interactive virtual telerehabilitation system in patients after total knee arthoplasty: a randomized controlled trial. Journal of Rehabilitation Medicine, vol. 45, pp. 392-396, 2013.
  14. C. Metcalf, R. Robinson, A. Malpass, T. Bogle, T. Dell, C. Harris, Markerless motion capture and measurement of hand kinematics: validation and application to home-based upper limb rehabilitation. IEEE Transactions on Biomedical Engineering, vol. 60 (8), pp. 2184 – 2192, 2013.
  15. S. M. Linder, A. B. Rosenfeldt, A. Reiss, S. Buchanan, K. Sahu, C. R. Bay, et al. The home stroke rehabilitation and monitoring system trial: a randomized controlled trial, International journal of stroke, vol. 8, pp. 46–53, 2013.
  16. D. O. Andrade, G. Fernandes, M. Junior, V. C. Roma, R. C. Joaquim, G.A.P. Caurin, Rehabilitation Robotics and Serious Games : An Initial Architecture for Simultaneous Players. Proceedings In Bioseñales y Biorobotics Conferencias (Rio de Janeiro, Brasil), pp. 1–6, 2013.
  17. G. Ligorio and A. M. Sabatini, “Extended Kalman filter-based methods for pose estimation using visual, inertial and magnetic sensors: comparative analysis and performance evaluation.,” Sensors (Basel)., vol. 13 (2), pp. 1919–41, 2013.
  18. G. Roussel, L. Bourgois, M. Benjelloun, and G. Delmaire, “Estimation of a semi-physical GLBE model using dual EnKF learning algorithm coupled with a sensor network design strategy: Application to air field monitoring,” Inf. Fusion, vol. 14(4), pp. 335–348, 2013.
  19. Zhang H, Dai G, Sun J, and Zhao Y. Unscented Kalman filter and its nonlinear application for tracking a moving target. Optik - International Journal for Light and Electron Optics, vol 124(20), pp. 4468–4471, 2013.

Downloads

Download data is not yet available.

Similar Articles

You may also start an advanced similarity search for this article.