Effect of Impairment on Upper Limb Performance in an Ageing Sample Population

  • Newton Howard
  • Ross Pollock
  • Joe Prinold
  • Joydeep Sinha
  • Di Newham
  • Jeroen Bergmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8010)


Ageing and age-related impairments have a detrimental effect on human performance and are likely to affect gesture based Human-Computer Interaction (HCI). Relying on “healthy” individuals to define gestures used for interfacing is likely to bias HCI design within the older population. To what extent gestures are affected by a common ageing disease remains to be determined. The aim of this study is to explore spatial and temporal changes in shoulder motion between rotator cuff patients and “healthy” controls. Seven controls and eight pre-operative patients participated in this study and performed several predefined gestures. The results show that the ROM and speed of movement can be affected by a common age-related disease. Wavelet analysis indicated that patients have a higher level of coupling between conditions making it harder to differentiate between different gestures. These results highlight the need to include age-related disabilities in HCI study populations.


Human Gesture Pattern Recognition Ageing Rotator Cuff Injury Wavelet Analysis 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Weber, I., et al.: Is the MS Kinect suitable for motion analysis? Biomed. Tech. (Berl) (August 30, 2012)Google Scholar
  2. 2.
    Yu, Y., et al.: Tactile gesture recognition for people with disabilities. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2005), vol. 5, pp. v/461–v/464 (2005)Google Scholar
  3. 3.
    Bergmann, J.H.M., et al.: Procedural differences directly effect Timed Up and Go times. Journal of the American Geriatrics Society 57, 2168–2169 (2009)CrossRefGoogle Scholar
  4. 4.
    Studenski, S., et al.: Gait Speed and Survival in Older Adults. JAMA: The Journal of the American Medical Association 305, 50–58 (2011)CrossRefGoogle Scholar
  5. 5.
    Zajicek, M.: Aspects of HCI research for older people. Universal Access in the Information Society 5, 279–286 (2006)CrossRefGoogle Scholar
  6. 6.
    Elgendi, F.P.M., Magenant-Thalmann, N.: Real-Time Speed Detection of Hand Gesture using Kinect. In: Workshop on Autonomous Social Robots and Virtual Humans, The 25th Annual Conference on Computer Animation and Social Agents (CASA 2012), Singapore, May 9-11 (2012)Google Scholar
  7. 7.
    Williams Jr., G.R., et al.: Rotator Cuff Tears: Why Do We Repair Them?*. The Journal of Bone & Joint Surgery 86, 2764–2776 (2004)Google Scholar
  8. 8.
    Murray, I.A., Johnson, G.R.: A study of the external forces and moments at the shoulder and elbow while performing every day tasks. Clinical Biomechanics 19, 586–594 (2004)CrossRefGoogle Scholar
  9. 9.
    Gamage, S.S.H.U., Lasenby, J.: New least squares solutions for estimating the average centre of rotation and the axis of rotation. Journal of Biomechanics 35, 87–93 (2002)CrossRefGoogle Scholar
  10. 10.
    Wu, G., et al.: ISB recommendation on definitions of joint coordinate systems of various joints for the reporting of human joint motion–Part II: shoulder, elbow, wrist and hand. Journal of Biomechanics 38, 981–992 (2005)CrossRefGoogle Scholar
  11. 11.
    Karduna, A.R., et al.: Dynamic measurements of three-dimensional scapular kinematics: a validation study. J. Biomech. Eng. 123, 184–190 (2001)CrossRefGoogle Scholar
  12. 12.
    Prinold, J.A.I., et al.: Skin-fixed scapula trackers: A comparison of two dynamic methods across a range of calibration positions. Journal of Biomechanics 44, 2004–2007 (2011)CrossRefGoogle Scholar
  13. 13.
    Vacha, L., Barunik, J.: Co-movement of energy commodities revisited: Evidence from wavelet coherence analysis. Energy Economics 34, 241–247 (2012)CrossRefGoogle Scholar
  14. 14.
    Schneider, K., Vasilyev, O.V.: Wavelet Methods in Computational Fluid Dynamics*. Annual Review of Fluid Mechanics 42, 473–503 (2010)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Larson, D.R.: Unitary systems, wavelet sets, and operator-theoretic interpolation of wavelets and frames. eprint arXiv:math/0604615, vol. 04 (2006)Google Scholar
  16. 16.
    Bergmann, J.H., et al.: Contribution of the reverse endoprosthesis to glenohumeral kinematics. Clin. Orthop. Relat. Res. 466, 594–598 (2008)CrossRefGoogle Scholar
  17. 17.
    Lachaux, J.-P., et al.: Estimating the time-course of coherence between single-trial brain signals: an introduction to wavelet coherence. Neurophysiologie Clinique/Clinical Neurophysiology 32, 157–174 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Newton Howard
    • 1
  • Ross Pollock
    • 2
  • Joe Prinold
    • 3
  • Joydeep Sinha
    • 4
  • Di Newham
    • 2
  • Jeroen Bergmann
    • 2
    • 5
  1. 1.Synthetic Intelligence LabMITUSA
  2. 2.Centre of Human & Aerospace Physiological SciencesKing’s College LondonUK
  3. 3.Department of BioengineeringImperial College LondonUK
  4. 4.Trauma & OrthopaedicsKing’s College HospitalUK
  5. 5.Medical Engineering Solutions in Osteoarthritis Centre of ExcellenceImperial College LondonUK

Personalised recommendations