[go: up one dir, main page]
Skip to main content
SpringerLink
Log in

Analysis of multiple waveforms by means of functional principal component analysis: normal versus pathological patterns in sit-to-stand movement

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

This paper presents an application of functional principal component analysis (FPCA) to describe the inter-subject variability of multiple waveforms. This technique was applied to the study of sit-to-stand movement in two groups of people, osteoarthritic patients and healthy subjects. Although STS movement has not been extensively applied to the study of knee osteoarthritis, it can provide relevant information about the effect of osteoarthritis on knee joint function. Two waveforms, knee flexion angle and flexion moment, were analysed simultaneously. Instead of using the common multivariate approach we used the functional one, which allows working with continuous functions with neither discretization nor time-scale normalization. The results show that time-scale normalization can alter the FPCA solution. Furthermore, FPCA presents better discriminatory power compared with the classical multivariate approach. This technique can, therefore, be applied as a functional assessment tool, allowing the identification of relevant variables to discriminate heterogeneous groups such as healthy and pathological subjects.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Arborelius UP, Wretenberg P, Lindberg F (1992) The effects of armrests and high seat heights on lower-limb joint load and muscular activity during sitting and rising. Ergonomics 35(11):1377–1391

    Article  Google Scholar 

  2. Astephen JL, Deluzio KJ (2005) Changes in frontal plane dynamics and the loading response phase of the gait cycle are characteristic of severe knee osteoarthritis application of a multidimensional analysis technique. Clin Biomech 20(2):209–217. doi:10.1016/j.clinbiomech.2004.09.007

    Article  Google Scholar 

  3. Conover WJ (1998) Some methods based on ranks. Practical nonparametric statistics, 3rd edn. Wiley, New York, pp 269–467

    Google Scholar 

  4. Daffertshofer A, Lamoth CJ, Meijer OG, Beek PJ (2004) PCA in studying coordination and variability: a tutorial. Clin Biomech 19(4):415–428. doi:10.1016/j.clinbiomech.2004.01.005

    Article  Google Scholar 

  5. Deluzio KJ, Wyss UP, Costigan PA, Sorbie C, Zee B (1999) Gait assessment in unicompartmental knee arthroplasty patients: principal component modelling of gait waveforms and clinical status. Hum Mov Sci 18(5):701–711. doi:10.1016/S0167-9457(99)00030-5

    Article  Google Scholar 

  6. Deluzio KJ, Wyss UP, Zee B, Costigan PA, Serbie C (1997) Principal component models of knee kinematics and kinetics: normal vs. pathological gait patterns. Hum Mov Sci 16:201–217. doi:10.1016/S0167-9457(96)00051-6

    Article  Google Scholar 

  7. el Nahass B, Madson MM, Walker PS (1991) Motion of the knee after condylar resurfacing—an in vivo study. J Biomech 24(12):1107–1117

    Article  Google Scholar 

  8. Hughes MA, Myers BS, Schenkman ML (1996) The role of strength in rising from a chair in the functionally impaired elderly. J Biomech 29(12):1509–1513

    Google Scholar 

  9. Ikeda ER, Schenkman ML, Riley PO, Hodge WA (1991) Influence of age on dynamics of rising from a chair. Phys Ther 71(6):473–481

    Google Scholar 

  10. Ivanenko YP, Poppele RE, Lacquaniti F (2004) Five basic muscle activation patterns account for muscle activity during human locomotion. J Physiol 556(1):267–282. doi:10.1113/jphysiol.2003.057174

    Article  Google Scholar 

  11. Jevsevar DS, Riley PO, Hodge WA, Krebs DE (1993) Knee kinematics and kinetics during locomotor activities of daily living in subjects with knee arthroplasty and in healthy control subjects. Phys Ther 73(4):229–242

    Google Scholar 

  12. Lamoth CJ, Daffertshofer A, Meijer OG, Lorimer Moseley G, Wuisman PI, Beek PJ (2004) Effects of experimentally induced pain and fear of pain on trunk coordination and back muscle activity during walking. Clin Biomech 19(6):551–563. doi:10.1016/j.clinbiomech.2003.10.006

    Article  Google Scholar 

  13. Leardini A, Chiari L, Della Croce U, Cappozzo A (2005) Human movement analysis using stereophotogrammetry. Part 3. Soft tissue artifact assessment and compensation. Gait Posture 21(2):212–225. doi:10.1016/j.gaitpost.2004.05.002

    Article  Google Scholar 

  14. Li ZM, Tang J (2007) Coordination of thumb joints during opposition. J Biomech 40(3):502–510. doi:10.1016/j.jbiomech.2006.02.019

    Article  Google Scholar 

  15. Mizner RL, Snyder-Mackler L (2005) Altered loading during walking and sit-to-stand is affected by quadriceps weakness after total knee arthroplasty. J Orthop Res 23(5):1083–1090. doi:10.1016/j.orthres.2005.01.021

    Article  Google Scholar 

  16. Mouchnino L, Mille ML, Martin N, Baroni G, Cincera M, Bardot A et al (2006) Behavioral outcomes following below-knee amputation in the coordination between balance and leg movement. Gait Posture 24(1):4–13. doi:10.1016/j.gaitpost.2005.07.007

    Article  Google Scholar 

  17. Page A, Ayala G, Leon MT, Peydro MF, Prat JM (2006) Normalizing temporal patterns to analyze sit-to-stand movements by using registration of functional data. J Biomech 39(13):2526–2534. doi:10.1016/j.jbiomech.2005.07.032

    Article  Google Scholar 

  18. Page A, Epifanio I (2007) A simple model to analyze the effectiveness of linear time normalization to reduce variability in human movement analysis. Gait Posture 25(1):153–156. doi:10.1016/j.gaitpost.2006.01.006

    Article  Google Scholar 

  19. Ramsay J, Dalzell C (1991) Some tools for functional data analysis. J R Stat Soc Ser B Methodol 3(3):539–572

    MathSciNet  Google Scholar 

  20. Ramsay JO, Silverman BW (2002) Applied functional data analysis: methods and case studies. Springer, New York

    MATH  Google Scholar 

  21. Ramsay JO, Silverman BW (2005) Functional data analysis. Springer, New York

    Google Scholar 

  22. Raptopoulos LSC, Dutra MS, Pinto FANC, de Pina Filho AC (2006) Alternative approach to modal gait analysis through the Karhunen–Loève decomposition: an application in the sagittal plane. J Biomech 39(15):2898–2906. doi:10.1016/j.jbiomech.2005.09.017

    Article  Google Scholar 

  23. Rodosky MW, Andriacchi TP, Andersson GB (1989) The influence of chair height on lower limb mechanics during rising. J Orthop Res 7(2):266–271

    Article  Google Scholar 

  24. Saari T, Tranberg R, Zugner R, Uvehammer J, Karrholm J (2004) The effect of tibial insert design on rising from a chair; motion analysis after total knee replacement. Clin Biomech 19(9):951–956. doi:10.1016/j.clinbiomech.2004.06.010

    Article  Google Scholar 

  25. Sadeghi H, Sadeghi S, Prince F, Allard P, Labelle H, Vaughan CL (2001) Functional roles of ankle and hip sagittal muscle moments in able-bodied gait. Clin Biomech 16(8):688–695. doi:10.1016/S0268-0033(01)00058-4

    Article  Google Scholar 

  26. Schultz AB, Alexander NB, Ashton-Miller JA (1992) Biomechanical analyses of rising from a chair. J Biomech 25(12):1383–1391

    Article  Google Scholar 

  27. Su FC, Lai KA, Hong WH (1998) Rising from chair after total knee arthroplasty. Clin Biomech 13(3):176–181. doi:10.1016/S0268-0033(97)00039-9

    Article  Google Scholar 

  28. Troje NF (2002) Decomposing biological motion: a framework for analysis and synthesis of human gait patterns. J Vis 2(5):371–387

    Article  Google Scholar 

  29. Wrigley AT, Albert WJ, Deluzio KJ, Stevenson JM (2005) Differentiating lifting technique between those who develop low back pain and those who do not. Clin Biomech 20(3):254–263. doi:10.1016/j.clinbiomech.2004.11.008

    Article  Google Scholar 

  30. Wrigley AT, Albert WJ, Deluzio KJ, Stevenson JM (2006) Principal component analysis of lifting waveforms. Clin Biomech 21(6):567–578. doi:10.1016/j.clinbiomech.2006.01.004

    Article  Google Scholar 

Download references

Acknowledgments

This study has been partially supported by Spanish Government Grant DPI2006-14722-C02-01 (cofinanced by EU FEDER funds), CICYT MTM2005-08689-C02-02, TIN2006-10134 and Fundació Caixa Castelló P11B2004-15.

Author information

Authors and Affiliations

  1. Departament de Matemàtiques, Universitat Jaume I, Castellón, Spain

    Irene Epifanio

  2. Instituto de Biomecánica de Valencia, Universidad Politécnica de Valencia, Camino de Vera, s/n, 46022, Valencia, Spain

    Carolina Ávila, Álvaro Page & Carlos Atienza

Authors
  1. Irene Epifanio
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carolina Ávila
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Álvaro Page
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Carlos Atienza
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Álvaro Page.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Epifanio, I., Ávila, C., Page, Á. et al. Analysis of multiple waveforms by means of functional principal component analysis: normal versus pathological patterns in sit-to-stand movement. Med Biol Eng Comput 46, 551–561 (2008). https://doi.org/10.1007/s11517-008-0339-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-008-0339-6

Keywords

Search

Navigation