Calculating Reachable Workspace Volume for Use in Quantitative Medicine

  • Robert Peter Matthew
  • Gregorij Kurillo
  • Jay J. Han
  • Ruzena Bajcsy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8927)

Abstract

Quantitative measures of the space an individual can reach is essential for tracking the progression of a disease and the effects of therapeutic intervention. The reachable workspace can be used to track an individuals’ ability to perform activities of daily living, such as feeding and grooming. There are few methods for quantifying upper limb performance, none of which are able to generate a reachable workspace volume from motion capture data. We introduce a method to estimate the reachable workspace volume for an individual by capturing their observed joint limits using a low cost depth camera. This method is then tested on seven individuals with varying upper limb performance. Based on these initial trials, we found that the reachable workspace volume decreased as muscular impairment increased. This shows the potential for this method to be used as a quantitative clinical assessment tool.

Keywords

Kinect Muscular dystrophy Functional workspace Rehabilitation Assessment Diagnosis Goniometry Skeletal modelling 

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References

  1. 1.
    Gajdosik, R.L., Bohannon, R.W.: Clinical measurement of range of motion: Review of goniometry emphasizing reliability and validity. Physical Therapy 67(12), 1867–1872 (1987)Google Scholar
  2. 2.
    Macedo, L.G., Magee, D.J.: Differences in range of motion between dominant and non-dominant sides of upper and lower extremities. Journal of manipulative and physiological therapeutics 31(8), 577–582 (2008)CrossRefGoogle Scholar
  3. 3.
    Bohannon, R.: Manual muscle test scores and dynamometer test scores of knee extension strength. Archives of physical medicine and rehabilitation 67(6), 390–392 (1986)Google Scholar
  4. 4.
    Escolar, D., Henricson, E., Mayhew, J., Florence, J., Leshner, R., Patel, K., Clemens, P.: Clinical evaluator reliability for quantitative and manual muscle testing measures of strength in children. Muscle & nerve 24(6), 787–793 (2001)CrossRefGoogle Scholar
  5. 5.
    Brooke, M.H., Griggs, R.C., Mendell, J.R., Fenichel, G.M., Shumate, J.B., Pellegrino, R.J.: Clinical trial in duchenne dystrophy. i. the design of the protocol. Muscle & nerve 4(3), 186–197 (1981)CrossRefGoogle Scholar
  6. 6.
    Beaton, D.E., Katz, J.N., Fossel, A.H., Wright, J.G., Tarasuk, V., Bombardier, C.: Measuring the whole or the parts?: Validity, reliability, and responsiveness of the disabilities of the arm, shoulder and hand outcome measure in different regions of the upper extremity. Journal of Hand Therapy 14(2), 128–142 (2001). The Outcome IssueCrossRefGoogle Scholar
  7. 7.
    Lamperti, C., Fabbri, G., Vercelli, L., D’Amico, R., Frusciante, R., Bonifazi, E., Fiorillo, C., Borsato, C., Cao, M., Servida, M., et al.: A standardized clinical evaluation of patients affected by facioscapulohumeral muscular dystrophy: The fshd clinical score. Muscle & nerve 42(2), 213–217 (2010)CrossRefGoogle Scholar
  8. 8.
    Fugl-Meyer, A.R., Jääskö, L., Leyman, I., Olsson, S., Steglind, S.: The post-stroke hemiplegic patient. 1. a method for evaluation of physical performance. Scandinavian journal of rehabilitation medicine 7(1), 13–31 (1974)Google Scholar
  9. 9.
    Mathiowetz, V., Weber, K., Kashman, N., Volland, G.: Adult norms for the nine hole peg test of finger dexterity. Occupational Therapy Journal of Research (1985)Google Scholar
  10. 10.
    Sharpless, J.: The nine hole peg test of finger hand coordination for the hemiplegic patient. Mossman’s A Problem Orientated Approach to Stroke Rehabilitation (1982)Google Scholar
  11. 11.
    Duncan, P.W., Weiner, D.K., Chandler, J., Studenski, S.: Functional reach: A new clinical measure of balance. Journal of Gerontology 45(6), M192–M197 (1990)CrossRefGoogle Scholar
  12. 12.
    Enright, P.L.: The six-minute walk test. Respiratory care 48(8), 783–785 (2003)Google Scholar
  13. 13.
    Zhang, Z.: Microsoft kinect sensor and its effect. IEEE MultiMedia 19(2), 4–10 (2012)CrossRefGoogle Scholar
  14. 14.
    Bo, A., Hayashibe, M., Poignet, P., et al.: Joint angle estimation in rehabilitation with inertial sensors and its integration with kinect. In: EMBC 2011: 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3479–3483 (2011)Google Scholar
  15. 15.
    Fern’ndez-Baena, A., Susin, A., Lligadas, X.: Biomechanical validation of upper-body and lower-body joint movements of kinect motion capture data for rehabilitation treatments. In: 2012 4th International Conference on Intelligent Networking and Collaborative Systems (INCoS), pp. 656–661, September 2012Google Scholar
  16. 16.
    Chang, C.Y., Lange, B., Zhang, M., Koenig, S., Requejo, P., Somboon, N., Sawchuk, A., Rizzo, A.: Towards pervasive physical rehabilitation using microsoft kinect. In: 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), pp. 159–162, May 2012Google Scholar
  17. 17.
    Khoshelham, K., Elberink, S.O.: Accuracy and resolution of kinect depth data for indoor mapping applications. Sensors 12(2), 1437–1454 (2012)CrossRefGoogle Scholar
  18. 18.
    Chang, Y.J., Chen, S.F., Huang, J.D.: A kinect-based system for physical rehabilitation: A pilot study for young adults with motor disabilities. Research in Developmental Disabilities 32(6), 2566–2570 (2011)CrossRefGoogle Scholar
  19. 19.
    Lange, B., Chang, C.Y., Suma, E., Newman, B., Rizzo, A., Bolas, M.: Development and evaluation of low cost game-based balance rehabilitation tool using the microsoft kinect sensor. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, pp. 1831–1834, August 2011Google Scholar
  20. 20.
    Lange, B., Koenig, S., McConnell, E., Chang, C., Juang, R., Suma, E., Bolas, M., Rizzo, A.: Interactive game-based rehabilitation using the microsoft kinect. In: Virtual Reality Short Papers and Posters (VRW), pp. 171–172, 2012 IEEE, March 2012Google Scholar
  21. 21.
    Pastor, I., Hayes, H., Bamberg, S.: A feasibility study of an upper limb rehabilitation system using kinect and computer games. In: Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, pp. 1286–1289, August 2012Google Scholar
  22. 22.
    Uzor, S., Baillie, L.: Exploring & designing tools to enhance falls rehabilitation in the home. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. CHI 2013, pp. 1233–1242. ACM, New York (2013)Google Scholar
  23. 23.
    Anton, D., Goni, A., Illarramendi, A., Torres-Unda, J., Seco, J.: Kires: A kinect-based telerehabilitation system. In: 2013 IEEE 15th International Conference on e-Health Networking, Applications Services (Healthcom), pp. 444–448, October 2013Google Scholar
  24. 24.
    Li, S., Xi, Z.: The measurement of functional arm reach envelopes for young chinese males. Ergonomics 33(7), 967–978 (1990)CrossRefMathSciNetGoogle Scholar
  25. 25.
    Book, A.S.: Vol. 1: Anthropometry for designers. NASA Reference Publication 1024 (1978)Google Scholar
  26. 26.
    Sengupta, A.K., Das, B.: Maximum reach envelope for the seated and standing male and female for industrial workstation design. Ergonomics 43(9), 1390–1404 (2000)CrossRefGoogle Scholar
  27. 27.
    Klopčar, N., Lenarčič, J.: Kinematic model for determination of human arm reachable workspace. Meccanica 40(2), 203–219 (2005)CrossRefMATHMathSciNetGoogle Scholar
  28. 28.
    Klopčar, N., Tomšič, M., Lenarčič, J.: A kinematic model of the shoulder complex to evaluate the arm-reachable workspace. Journal of biomechanics 40(1), 86–91 (2007)CrossRefGoogle Scholar
  29. 29.
    Kurillo, G., Han, J.J., Obdrzálek, S., Yan, P., Abresch, R.T., Nicorici, A., Bajcsy, R.: Upper extremity reachable workspace evaluation with kinect. In: MMVR, pp. 247–253 (2013)Google Scholar
  30. 30.
    Kurillo, G., Han, J., Nicorici, A., Johnson, L., Abresch, R., Henricson, E., McDonald, C., Bajcsy, R.: Upper extremity reachable workspace evaluation in DMD using kinect. Neuromuscular Disorders 23(910), 749–750 (2013). 18th International Congress of The World Muscle SocietyCrossRefGoogle Scholar
  31. 31.
    Kurillo, G., Chen, A., Bajcsy, R., Han, J.J.: Evaluation of upper extremity reachable workspace using kinect camera. Technology and Health Care 21(6), 641–656 (2013)Google Scholar
  32. 32.
    Han, J.J., Kurillo, G., Abresch, R.T., de Bie, E., Nicorici, A., Bajcsy, R.: Reachable workspace in facioscapulohumeral muscular dystrophy (fshd) by kinect. Muscle & nerve (2014)Google Scholar
  33. 33.
    Siciliano, B., Khatib, O.: Springer Handbook of Robotics. Springer (2008)Google Scholar
  34. 34.
    Khalil, W., Dombre, E.: Modeling. Butterworth-Heinemann, Identification and Control of Robots (2004)Google Scholar
  35. 35.
    Edelsbrunner, H., Mücke, E.P.: Three-dimensional alpha shapes. ACM Trans. Graph. 13(1), 43–72 (1994)CrossRefMATHGoogle Scholar
  36. 36.
    Burdick, J.W.: Kinematic Analysis and Design of Redundant Robot Manipulators. PhD thesis, Stanford University (1988)Google Scholar
  37. 37.
    Wu, G., Van Der Helm, F.C., Veeger, H., Makhsous, M., Van Roy, P., Anglin, C., Nagels, J., Karduna, A.R., McQuade, K., Wang, X., et al.: Isb recommendation on definitions of joint coordinate systems of various joints for the reporting of human joint motionpart ii: Shoulder, elbow, wrist and hand. Journal of biomechanics 38(5), 981–992 (2005)CrossRefGoogle Scholar
  38. 38.
    Biodigital: Biodigital human (06 2014)Google Scholar
  39. 39.
    Julier, S.J., Uhlmann, J.K.: A new extension of the kalman filter to nonlinear systems. In: Int. symp. aerospace/defense sensing, simul. and controls. vol. 3, pp. 3–2, Orlando (1997)Google Scholar
  40. 40.
    Wan, E.A., Van Der Merwe, R.: The unscented kalman filter for nonlinear estimation. In: Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000. AS-SPCC. The IEEE 2000, pp. 153–158. IEEE (2000)Google Scholar
  41. 41.
    Ziegler, J., Nickel, K., Stiefelhagen, R.: Tracking of the articulated upper body on multi-view stereo image sequences. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 774–781, June 2006Google Scholar
  42. 42.
    Butterworth, S.: On the theory of filter amplifiers. Wireless Engineer 7, 536–541 (1930)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Robert Peter Matthew
    • 1
  • Gregorij Kurillo
    • 1
  • Jay J. Han
    • 2
  • Ruzena Bajcsy
    • 1
  1. 1.Department of Electrical Engineering and Computer ScienceUniversity of California, BerkeleyBerkeleyUSA
  2. 2.Department of Physical Medicine and RehabilitationUniversity of CaliforniaDavis, SacramentoUSA

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