Skip to main content
SpringerLink
Log in

Implementation of Multiple Regression Technique for Detection of Gait Asymmetry Using Experimental Gait Data

  • Original Article
  • Published:
Journal of Medical and Biological Engineering Aims and scope Submit manuscript

Abstract

Purpose

This study reports the implementation of different regression (multivariate and step-wise) techniques towards determining the gait asymmetry and comparison with the symmetry indices (SI) values.

Methods

To predict the gait asymmetry between two different legs, a set of gait trials (thirty-five participants) via a three-dimensional motion capture setup and force platform available at the parent institute, has been acquired. Two separate regression fit models are prepared to indicate the significant gait parameters for the right and left legs utilizing the recorded foot data. The significant sets of coefficients for the right and left leg parameters are compared with the SI values to validate the gait asymmetry.

Results

The calculated mean SI from the experimental results correspond to the predicted regression model responses, and 18 of the 27 regression fits present different sets of significant coefficients for the right and left leg parameters.

Conclusion

The regression fits show gait parameter dependency on the different sets of predictor variables. The method can be adopted for different patient data sets to detect the influence of the causative factors on pathologic gait.

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

Access this article

Log in via an institution

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
Fig. 4

Similar content being viewed by others

Abbreviations

Fz1, Fz0, Fz2 :

The peak and trough values of the vertical ground reaction forces of a normal healthy walking trial

Fx1, Fx2 :

The peak and trough forces of the antero-posterior ground reaction forces

AA1−2 :

The peak ankle angle values at 0–50% and 50–100% of gait cycle

KA1−2 :

The peak knee angle values at 0–30% and 30–100% of gait cycle

HA1−3 :

The peak hip angle values at 0–25%, 25–75% and 75–100% of gait cycle

AM1−2 :

The peak ankle moment values at 0–30% and 30–70% of gait cycle

KM1−3 :

The peak knee moment values at 0–30%, 20–60% and 40–100% of gait cycle

HM1−2 :

The peak hip moment values at 0–30% and 30–70% of gait cycle

AP1−2 :

The peak ankle power values at 0–25% and 25–75% of gait cycle

KP1−3 :

The peak knee power values at 0–20%, 40–80% and 80–100% of gait cycle

HP1−3 :

The peak knee power values at 0–30%, 30–60% and 60–100% of gait cycle

x 1 to x 5 :

The coefficients corresponding to the independent variables (age, height, weight, leg length and walking speed respectively) for the multivariate and step-wise multivariate regression fits

R 2 value:

A statistical measure of how close the data points are to the regression fits. Its values lie between 0 to 1 (or 0–100%), 1 representing the ideal situation wherein all the data points lie on the fitted line

p-value:

Statistical significance of the data (smaller the p-value, greater the significance)

F-value:

The variance of the data for the predicted model

References

  1. Stansfield, B., Hillman, S., Hazlewood, M., & Robb, J. (2006). Regression analysis of gait parameters with speed in normal children walking at self-selected speeds. Gait & Posture, 23(3), 288–294.

    Article  Google Scholar 

  2. Olney, S. J., Griffin, M. P., & McBride, I. D. (1994). Temporal, kinematic, and kinetic variables related to gait speed in subjects with hemiplegia: A regression approach. Physical Therapy, 74(9), 872–885.

    Article  Google Scholar 

  3. Jena, S., Sakhare, G. M., Panda, S. K., & Thirugnanam, A. (2017). Evaluation and prediction of human gait parameters using univariate, multivariate and stepwise statistical methods. Journal of Mechanics in Medicine and Biology, 17(5), 1750076.

    Article  Google Scholar 

  4. Lee, T. H., Tsuchida, T., Kitahara, H., & Moriya, H. (1999). Gait analysis before and after unilateral total knee arthroplasty. Study using a linear regression model of normal controls—women without arthropathy. Journal of Orthopaedic Science, 4(1), 13–21.

    Article  Google Scholar 

  5. Santhiranayagam, B. K., Lai, D., Shilton, A., Begg, R., Palaniswami, M. (2011). Regression models for estimating gait parameters using inertial sensors. In: 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing Adelaide, SA, Australia, IEEE, pp. 46–51.

  6. Haddad, J. M., Rietdyk, S., Ryu, J. H., Seaman, J. M., Silver, T. A., Kalish, J. A., & Hughes, C. M. (2011). Postural asymmetries in response to holding evenly and unevenly distributed loads during self-selected stance. Journal of Motor Behavior, 43(4), 345–355.

    Article  Google Scholar 

  7. Sadeghi, H., Allard, P., Prince, F., & Labelle, H. (2000). Symmetry and limb dominance in able-bodied gait: A review. Gait & Posture, 12(1), 34–45.

    Article  Google Scholar 

  8. Herzog, W., Nigg, B. M., Read, L. J., & Olsson, E. (1989). Asymmetries in ground reaction force patterns in normal human gait. Medicine and Science in Sports and Exercise, 21(1), 110–114.

    Article  Google Scholar 

  9. Cheung, J. T. M., Zhang, M., Leung, A. K. L., & Fan, Y.-B. (2005). Three-dimensional finite element analysis of the foot during standing: A material sensitivity study. Journal of Biomechanics, 38(5), 1045–1054.

    Article  Google Scholar 

  10. Cho, J.-R., Park, S.-B., Ryu, S.-H., Kim, S.-H., & Lee, S.-B. (2009). Landing impact analysis of sports shoes using 3-D coupled foot-shoe finite element model. Journal of Mechanical Science and Technology, 23(10), 2583–2591.

    Article  Google Scholar 

  11. Ferris, D. P., Czerniecki, J. M., & Hannaford, B. (2005). An ankle-foot orthosis powered by artificial pneumatic muscles. Journal of Applied Biomechanics, 21(2), 189.

    Article  Google Scholar 

  12. Telfer, S., Woodburn, J., & Turner, D. E. (2014). Measurement of functional heel pad behaviour in-shoe during gait using orthotic embedded ultrasonography. Gait & Posture, 39(1), 328–332.

    Article  Google Scholar 

  13. Jena, S., Sudro, P. N., Reddy, P. V., Thirugnanam, A., & Panda, S. K. (2017). The effect of transient loading on a foot-orthotic using temporal parameters of gait. Journal of Mechanics in Medicine and Biology, 17(8), 1750117.

    Article  Google Scholar 

  14. Cheung, G., Zalzal, P., Bhandari, M., Spelt, J., & Papini, M. (2004). Finite element analysis of a femoral retrograde intramedullary nail subject to gait loading. Medical Engineering & Physics, 26(2), 93–108.

    Article  Google Scholar 

  15. Yazdani, M., Salarieh, H., & Saadat Foumani, M. (2018). Hierarchical decentralized control of a five-link biped robot. Scientia Iranica, 25(5), 2675–2692.

    Google Scholar 

  16. Ducharme, S. W., & van Emmerik, R. E. (2019). Multifractality of unperturbed and asymmetric locomotion. Journal of Motor Behavior, 51(4), 394–405.

    Article  Google Scholar 

  17. Allard, P., Lachance, R., Aissaoui, R., & Duhaime, M. (1996). Simultaneous bilateral 3-D able-bodied gait. Human Movement Science, 15(3), 327–346.

    Article  Google Scholar 

  18. Sadeghi, H., Allard, P., & Duhaime, M. (1997). Functional gait asymmetry in able-bodied subjects. Human Movement Science, 16(2–3), 243–258.

    Article  Google Scholar 

  19. Wahid, F., Begg, R., McClelland, J. A., Webster, K. E., Halgamuge, S., & Ackland, D. C. (2016). A multiple regression normalization approach to evaluation of gait in total knee arthroplasty patients. Clinical Biomechanics, 32, 92–101.

    Article  Google Scholar 

  20. Van Hamme, A., El Habachi, A., Samson, W., Dumas, R., Cheze, L., & Dohin, B. (2015). Gait parameters database for young children: The influences of age and walking speed. Clinical Biomechanics, 30(6), 572–577.

    Article  Google Scholar 

  21. Ataee, O., Hafezi Moghaddas, N., Lashkari Pour, G. R., & Nooghabi, A. (2018). Predicting shear wave velocity of soil using multiple linear regression analysis and artificial neural networks. Scientia Iranica, 25(4), 1943–1955.

    Google Scholar 

  22. Santos, S., Soares, B., Leite, M., & Jacinto, J. (2017). Design and development of a customised knee positioning orthosis using low cost 3D printers. Virtual and Physical Prototyping, 12(4), 322–332.

    Article  Google Scholar 

  23. Błażkiewicz, M., Wiszomirska, I., & Wit, A. (2014). Comparison of four methods of calculating the symmetry of spatial-temporal parameters of gait. Acta of Bioengineering and Biomechanics, 16(1), 29–35.

    Google Scholar 

  24. Clark, R. A., Vernon, S., Mentiplay, B. F., Miller, K. J., McGinley, J. L., Pua, Y. H., et al. (2015). Instrumenting gait assessment using the Kinect in people living with stroke: Reliability and association with balance tests. Journal of Neuroengineering and Rehabilitation, 12(1), 15.

    Article  Google Scholar 

Download references

Acknowledgement

This study was supported by Centre of Excellence—Orthopaedic Tissue Engineering and Rehabilitation, under TEQIP II (Sanction order No. AC/TEQIP-II/CoE/13/603 dated 24th June 2013).

Author information

Authors and Affiliations

  1. Department of Biotechnology and Medical Engineering, National Institute of Technology, Rourkela, Odisha, 769008, India

    Shreeshan Jena, Gaurav Mohan Sakhare & A. Thirugnanam

  2. Department of Mechanical Engineering, National Institute of Technology, Rourkela, Odisha, 769008, India

    Subrata Kumar Panda

Authors
  1. Shreeshan Jena
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gaurav Mohan Sakhare
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Subrata Kumar Panda
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A. Thirugnanam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Subrata Kumar Panda.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jena, S., Sakhare, G.M., Panda, S.K. et al. Implementation of Multiple Regression Technique for Detection of Gait Asymmetry Using Experimental Gait Data. J. Med. Biol. Eng. 41, 1–10 (2021). https://doi.org/10.1007/s40846-020-00533-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40846-020-00533-8

Keywords

Search

Navigation