Advertisement

Multi-camera systems for rehabilitation therapies: a study of the precision of Microsoft Kinect sensors

  • Miguel Oliver
  • Francisco Montero
  • José Pascual Molina
  • Pascual González
  • Antonio Fernández-Caballero
Article

Abstract

This paper seeks to determine how the overlap of several infrared beams affects the tracked position of the user, depending on the angle of incidence of light, distance to the target, distance between sensors, and the number of capture devices used. We also try to show that under ideal conditions using several Kinect sensors increases the precision of the data collected. The results obtained can be used in the design of telerehabilitation environments in which several RGB-D cameras are needed to improve precision or increase the tracking range. A numerical analysis of the results is included and comparisons are made with the results of other studies. Finally, we describe a system that implements intelligent methods for the rehabilitation of patients based on the results of the tests carried out.

Keywords

Kinect sensor Rehabilitation system Capture precision Multi-camera system 

CLC number

TP391 

References

  1. Bonnechère, B., Jansen, B., Salvia, P., et al., 2014. Determination of the precision and accuracy of morphological measurements using the KinectTM sensor: comparison with standard stereophotogrammetry. Ergonomics, 57(4):622–631. http://dx.doi.org/10.1080/00140139.2014.884246CrossRefGoogle Scholar
  2. Chang, Y., Chen, S., Huang, J., 2011. A Kinect-based system for physical rehabilitation: a pilot study for young adults with motor disabilities. Res. Deve. Disabil., 32(6):2566–2570. http://dx.doi.org/10.1016/j.ridd.2011.07.002CrossRefGoogle Scholar
  3. Essmaeel, K., Gallo, L., Damiani, E., et al., 2012. Temporal denoising of Kinect depth data. Proc. 8th Int. Conf. on Signal Image Technology and Internet Based Systems, p.47–52. http://dx.doi.org/10.1109/SITIS.2012.18Google Scholar
  4. Essmaeel, K., Gallo, L., Damiani, E., et al., 2014. Comparative evaluation of methods for filtering Kinect depth data. Multim. Tools Appl., 74(17):7331–7354. http://dx.doi.org/10.1007/s11042-014-1982-6CrossRefGoogle Scholar
  5. Fernández-Baena, A., Susín, A., Lligadas, X., 2012. Biomechanical validation of upper-body and lower-body joint movements of Kinect motion capture data for rehabilitation treatments. Proc. 4th Int. Conf. on Intelligent Networking and Collaborative Systems, p.656–661. http://dx.doi.org/10.1109/iNCoS.2012.66Google Scholar
  6. Freitas, D., da Gama, A., Figueiredo, L., et al., 2012. Development and evaluation of a Kinect based motor rehabilitation game. Proc. SBGames, p.144–153.Google Scholar
  7. Gonzalez-Jorge, H., Riveiro, B., Vazquez-Fernandez, E., et al., 2013. Metrological evaluation of Microsoft Kinect and Asus Xtion sensors. Measurement, 46(6):1800–1806. http://dx.doi.org/10.1016/j.measurement.2013.01.011CrossRefGoogle Scholar
  8. Haggag, H., Hossny, M., Filippidis, D., et al., 2013. Measuring depth accuracy in RGBD cameras. Proc. 7th Int. Conf. on Signal Processing and Communication Systems, p.1–7. http://dx.doi.org/10.1109/ICSPCS.2013.6723971Google Scholar
  9. Khoshelham, K., Elberink, S.O., 2012. Accuracy and resolution of Kinect depth data for indoor mapping applications. Sensors, 12(2):1437–1454. http://dx.doi.org/10.3390/s120201437CrossRefGoogle Scholar
  10. Mallick, T., Das, P.P., Majumdar, A.K., 2014. Characterizations of noise in Kinect depth images: a review. IEEE Sens. J., 14(6):1731–1740. http://dx.doi.org/10.1109/JSEN.2014.2309987CrossRefGoogle Scholar
  11. Mkhitaryan, A., Burschka, D., 2013. RGB-D sensor data correction and enhancement by introduction of an additional RGB view. Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, p.1077–1083. http://dx.doi.org/10.1109/IROS.2013.6696484Google Scholar
  12. Olesen, S.M., Lyder, S., Kraft, D., et al., 2015. Real-time extraction of surface patches with associated uncertainties by means of Kinect cameras. J. Real-Time Image Process., 10(1):105–118. http://dx.doi.org/10.1007/s11554-012-0261-xCrossRefGoogle Scholar
  13. Oliver, M., Molina, J.P., Montero, F., et al., 2014a. A multisensor system for positioning of multiple users. Proc. XV Int. Conf. on Human Computer Interaction, Article 59. http://dx.doi.org/10.1145/2662253.2662312Google Scholar
  14. Oliver, M., Molina, J.P., Montero, F., et al., 2014b. Wireless multisensory interaction in an intelligent rehabilitation environment. Proc. 5th Int. Symp. on Ambient Intelligence, p.193–200. http://dx.doi.org/10.1007/978-3-319-07596-9_21Google Scholar
  15. Oliver, M., Montero, F., Molina, J.P., et al., 2015a. How many Kinects should look at you? A multi-agent system approach. Proc. 13th Int. Conf. on Practical Applications of Agents and Multi-Agent Systems, p.105–112. http://dx.doi.org/10.1007/978-3-319-19629-9_12Google Scholar
  16. Oliver, M., Montero, F., Fernández-Caballero, A., et al., 2015b. RGB-D assistive technologies for acquired brain injury: description and assessment of user experience. Expert Syst., 32(3):370–380. http://dx.doi.org/10.1111/exsy.12096CrossRefGoogle Scholar
  17. Regazzoni, D., de Vecchi, G., Rizzi, C., 2014. RGB cams vs RGB-D sensors: low cost motion capture technologies performances and limitations. J. Manuf. Syst., 33(4):719–728. http://dx.doi.org/10.1016/j.jmsy.2014.07.011CrossRefGoogle Scholar

Copyright information

© Journal of Zhejiang University Science Editorial Office and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Miguel Oliver
    • 1
  • Francisco Montero
    • 2
  • José Pascual Molina
    • 2
  • Pascual González
    • 2
  • Antonio Fernández-Caballero
    • 2
  1. 1.Instituto de Investigación en Informática de AlbaceteUniversidad de Castilla-La ManchaAlbaceteSpain
  2. 2.Departamento de Sistemas InformáticosUniversidad de Castilla-La ManchaAlbaceteSpain

Personalised recommendations