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Validation of Automated Mobility Assessment Using a Single 3D Sensor

  • Jiun-Yu Kao
  • Minh Nguyen
  • Luciano Nocera
  • Cyrus Shahabi
  • Antonio Ortega
  • Carolee Winstein
  • Ibrahim Sorkhoh
  • Yu-chen Chung
  • Yi-an Chen
  • Helen Bacon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9914)

Abstract

Reliable mobility assessment is essential to diagnose or optimize treatment in persons affected by mobility disorders, e.g., for musculo-skeletal disorders. In this work, we present a system that is able to automatically assess mobility using a single 3D sensor. We validate the system ability to assess mobility and predict the medication state of Parkinson’s disease patients while using a relatively small number of motion tasks. One key component of our system is a graph-based feature extraction technique that can capture the dynamic coordination between parts of the body while providing results that are easier to interpret than those obtained with other data-driven approaches. We further discuss the system and the study design, highlighting aspects that provide insights for developing mobility assessment applications in other contexts.

Keywords

Mobility assessment 3D Sensor Parkinson’s disease Human performance Classification 

References

  1. 1.
    Chen, L., Wei, H., Ferryman, J.: A survey of human motion analysis using depth imagery. Pattern Recogn. Lett. 34(15), 1995–2006 (2013)CrossRefGoogle Scholar
  2. 2.
    Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: extending high-dimensional data analysis to networks and other irregular domains. IEEE Sig. Process. Mag. 30(3), 83–98 (2013)CrossRefGoogle Scholar
  3. 3.
    Duin, R.P.W., Tax, D.M.J.: Experiments with classifier combining rules. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 16–29. Springer, Heidelberg (2000). doi: 10.1007/3-540-45014-9_2 CrossRefGoogle Scholar
  4. 4.
    Galna, B., Barry, G., Jackson, D., Mhiripiri, D., Olivier, P., Rochester, L.: Accuracy of the microsoft kinect sensor for measuring movement in people with parkinson’s disease. Gait Posture 39(4), 1062–1068 (2014)CrossRefGoogle Scholar
  5. 5.
    Galna, B., Jackson, D., Schofield, G., McNaney, R., Webster, M., Barry, G., Mhiripiri, D., Balaam, M., Olivier, P., Rochester, L.: Retraining function in people with parkinson disease using the microsoft kinect: game design and pilot testing. J. Neuroeng. Rehabil. 11(1), 60 (2014)CrossRefGoogle Scholar
  6. 6.
    Gowayyed, M.A., Torki, M., Hussein, M.E., El-Saban, M.: Histogram of oriented displacements (hod): describing trajectories of human joints for action recognition. In: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, pp. 1351–1357 (2013)Google Scholar
  7. 7.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  8. 8.
    Kao, J.Y., Ortega, A., Narayanan, S.: Graph-based approach for motion capture data representation and analysis. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 2061–2065, October 2014Google Scholar
  9. 9.
    Kashani, F.B., Medioni, G., Nguyen, K., Nocera, L., Shahabi, C., Wang, R., Blanco, C.E., Chen, Y.A., Chung, Y.C., Fisher, B., et al.: Monitoring mobility disorders at home using 3d visual sensors and mobile sensors. In: Proceedings of the 4th Conference on Wireless Health, p. 9. ACM (2013)Google Scholar
  10. 10.
    Kerola, T., Inoue, N., Shinoda, K.: Spectral graph skeletons for 3D action recognition. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9006, pp. 417–432. Springer, Heidelberg (2015)Google Scholar
  11. 11.
    Kittler, J., Hatef, M., Duin, R.P., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)CrossRefGoogle Scholar
  12. 12.
    Luo, J., Wang, W., Qi, H.: Group sparsity and geometry constrained dictionary learning for action recognition from depth maps. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 1809–1816 (2013)Google Scholar
  13. 13.
    McNeely, M.E., Duncan, R.P., Earhart, G.M.: Medication improves balance and complex gait performance in parkinson disease. Gait Posture 36(1), 144–148 (2012)CrossRefGoogle Scholar
  14. 14.
    Mirek, E., Rudzińska, M., Szczudlik, A.: The assessment of gait disorders in patients with parkinson’s disease using the three-dimensional motion analysis system vicon. Neurologia i neurochirurgia polska 41(2), 128–133 (2006)Google Scholar
  15. 15.
    Nguyen, M., Fan, L., Shahabi, C.: Activity recognition using wrist-worn sensors for human performance evaluation. In: The Sixth Workshop on Biological Data Mining and its Applications in Healthcare (2015)Google Scholar
  16. 16.
    Ofli, F., Chaudhry, R., Kurillo, G., Vidal, R., Bajcsy, R.: Sequence of the most informative joints (smij): a new representation for human skeletal action recognition. J. Vis. Commun. Image Represent. 25(1), 24–38 (2014)CrossRefGoogle Scholar
  17. 17.
    Palacios-Navarro, G., García-Magariño, I., Ramos-Lorente, P.: A kinect-based system for lower limb rehabilitation in parkinson’s disease patients: a pilot study. J. Med. Syst. 39(9), 1–10 (2015)CrossRefGoogle Scholar
  18. 18.
    Pfister, A., West, A.M., Bronner, S., Noah, J.A.: Comparative abilities of microsoft kinect and vicon 3d motion capture for gait analysis. J. Med. Eng. Technol. 38(5), 274–280 (2014)CrossRefGoogle Scholar
  19. 19.
    Rokach, L.: Ensemble-based classifiers. Artif. Intell. Rev. 33(1–2), 1–39 (2010)CrossRefGoogle Scholar
  20. 20.
    Ruta, D., Gabrys, B.: Classifier selection for majority voting. Inf. Fusion 6(1), 63–81 (2005)CrossRefzbMATHGoogle Scholar
  21. 21.
    Shotton, J., Sharp, T., Kipman, A., Fitzgibbon, A., Finocchio, M., Blake, A., Cook, M., Moore, R.: Real-time human pose recognition in parts from single depth images. Commun. ACM 56(1), 116–124 (2013)CrossRefGoogle Scholar
  22. 22.
    Stone, E., Skubic, M.: Evaluation of an inexpensive depth camera for in-home gait assessment. J. Ambient Intell. Smart Env. 3(4), 349–361 (2011)Google Scholar
  23. 23.
    Takač, B., Català, A., Martín, D.R., Van Der Aa, N., Chen, W., Rauterberg, M.: Position and orientation tracking in a ubiquitous monitoring system for parkinson disease patients with freezing of gait symptom. JMIR mHealth and uHealth 1(2), e14 (2013)CrossRefGoogle Scholar
  24. 24.
    Wang, J., Liu, Z., Wu, Y., Yuan, J.: Mining actionlet ensemble for action recognition with depth cameras. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1290–1297 (2012)Google Scholar
  25. 25.
    Wang, R., Medioni, G., Winstein, C.J., Blanco, C.: Home monitoring musculo-skeletal disorders with a single 3d sensor. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 521–528. IEEE (2013)Google Scholar
  26. 26.
    Weiss, A., Sharifi, S., Plotnik, M., van Vugt, J.P., Giladi, N., Hausdorff, J.M.: Toward automated, at-home assessment of mobility among patients with parkinson disease, using a body-worn accelerometer. Neurorehabilitation Neural Repair 25(9), 810–818 (2011)CrossRefGoogle Scholar
  27. 27.
    Xia, L., Chen, C.C., Aggarwal, J.: View invariant human action recognition using histograms of 3d joints. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 20–27. IEEE (2012)Google Scholar
  28. 28.
    Yang, X., Tian, Y.: Eigenjoints-based action recognition using naive-bayes-nearest-neighbor. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 14–19. IEEE (2012)Google Scholar
  29. 29.
    Zanfir, M., Leordeanu, M., Sminchisescu, C.: The moving pose: an efficient 3d kinematics descriptor for low-latency action recognition and detection. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 2752–2759, December 2013Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jiun-Yu Kao
    • 1
  • Minh Nguyen
    • 1
  • Luciano Nocera
    • 1
  • Cyrus Shahabi
    • 1
  • Antonio Ortega
    • 1
  • Carolee Winstein
    • 1
  • Ibrahim Sorkhoh
    • 1
  • Yu-chen Chung
    • 1
  • Yi-an Chen
    • 1
  • Helen Bacon
    • 1
  1. 1.University of Southern CaliforniaLos AngelesUSA

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