1. Gait Symmetry With Application to Subjects with Multiple Sclerosis Stephanie Crenshaw, James Richards, Caralynne Miller Department of Health, Nutrition, and Exercise Sciences University of Delaware American College of Medicine 53 rd Annual Meeting May 31-June 3, 2006 Denver, Colorado

2. Gait Symmetry Modified by W. Rose from the original presentation to emphasize the trend symmetry measure. Trend symmetry values converted to new scale where +1=exact symmetry, -1=exact anti-symmetry, 0=no symmetry. Matlab code to compute trend symmetry and related quantities: trendsymmetry.m. Needs two input files, e.g. x1_trendsymtest.txt, x21…txt, or knee_abdang_R and …_L.txt, etc. Labview code to compute trend symmetry and related quantities: MainInteractiveWCR.vi. Needs one Orthotrak NRM file as input, e.g. 0.NRM.

3. Purposes  To explain newly developed Symmetry Analysis Method  To apply Symmetry Analysis Method to Clinical Population of Subjects with Multiple Sclerosis

4. Symmetry Definition:  Both limbs are behaving identically Measures of Symmetry  Symmetry Index  Symmetry Ratio  Statistical Methods

5. Symmetry Index When SI = 0, gait is symmetrical Differences are relative to average value. If a large asymmetry is present, the average value does not correctly reflect the performance of either limb Robinson RO, Herzog W, Nigg BM. Use of force platform variables to quantify the effects of chiropractic manipulation on gait symmetry. J Manipulative Physiol Ther 1987;10(4):172–6.

6. Symmetry Ratio Limitations: relatively small asymmetry and a failure to provide info regarding location of asymmetry Low sensitivity Seliktar R, Mizrahi J. Some gait characteristics of below-knee amputees and their reflection on the ground reaction forces. Eng Med 1986;15(1):27–34.

7. Statistical Measures of Symmetry Correlation Coefficients Principal Component Analysis Analysis of Variance Use single points or limited set of points Do not analyze the entire waveform Sadeghi H, et al. Symmetry and limb dominance in able-bodied gait: a review. Gait Posture 2000;12(1):34–45. Sadeghi H, Allard P, Duhaime M. Functional gait asymmetry in ablebodied subjects. Hum Movement Sci 1997;16:243–58.

8. New Method - Eigenvector Analysis The method proposed utilizes eigenvector analysis to compare time-normalized right leg gait cycles to time-normalized left leg gait cycles. Paired data points from the right and left waveforms are entered into an m row x 2 column matrix, where each pair of points is one of the m rows. Singular Value Decomposition (SVD) is then performed on this matrix to determine the principal and secondary eigenvectors.

9. Eigenvector Analysis Use eigenvector analysis to determine Waveform Trend Similarity Trend Similarity (or Symmetry) is defined as where +/- depends on slope of principal eigenvector (+ = symmetric, - = antisymmetric)

10. Additional Symmetry Measures Range ratio quantifies the difference in range of motion of each limb, and is calculated by dividing the range of motion of the right limb from that of the left limb. Range offset, a measure of the differences in operating range of each limb, is calculated by subtracting the average of the right side waveform from the average of the left side waveform.

11. Trend Symmetry Expressed as 1-(ratio of the variance about eigenvector to the variance along the eigenvector) Trend Symmetry: 0.948Range Amplitude Ratio: 0.79, Range Offset:0

12. Range Amplitude Ratio Expressed as a ratio of the range of motion of the left limb to that of the right limb Range Amplitude Ratio: 2.0Trend Symmetry: 1.0, Range Offset: 19.45

13. Range Offset Calculated by subtracting the average of the right side waveform from the average of the left side waveform Range Offset: 10.0Trend Symmetry: 1.0, Range Amplitude Ratio: 1.0

14. Final Adjustments Trend similarity can be used to estimate the phase relationship between waveforms. Phase-shift one waveform in 1-percent increments (e.g. sample 100 becomes sample 1, sample 1 becomes sample 2…), up to a max shift of +-20%. Compute trend similarity at each phase shift. Phase shift with greatest trend similarity is an estimate of the phase offset between the waves.

15. Symmetry Measures Trend Symmetry Quantifies waveform shape similarity Range Amplitude Ratio Quantifies differences in the range of motion of each limb Range Offset Quantifies differences in the mean position of each limb

16. Ankle Joint Trend Symmetry Phase Shift (% Cycle) Max Trend Symmetry Range Amplitude Range Offset 95% CI0.937-1.00-2.2 – 2.60 – 4.940.70 - 1.27-6.8 – 6.2 Unbraced0.991 0.893.8 Braced0.71-30.751.72-5.6 Amputee0.820 1.30-3.7 Symmetry Example…Ankle Joint These trend symmetry values are on the new scale, where +-1=perfect symmetry, 0=no symmetry.

17. Symmetry Measures Applied to Patients with MS Remainder of this presentation describes the application of the symmetry measures to subjects with MS and healthy controls.

18. Methods - Subjects 13 with MS  Age 44.4±10.6 yrs  Height 167.0±8.7 cm  Mass 79.1±20.1 kg  EDSS average 3.5 (range 2.5-4.5) 8 Healthy Controls  Age 40.9±9.6 yrs  Height 167.4±14.6 cm  Mass 72.6±14.2 kg

19. Methods – Data Collection Data Collection:  8 Motion-Analysis Cameras 60 Hz  2 AMTI Force Plates 960 Hz  2 Gait Analysis Conditions Fresh Fatigued

20. Methods – Data Analysis Created Ensemble averages of 15 gait cycles  sagittal plane kinematics for fresh and fatigued conditions Calculated Symmetry values  Affected/Unaffected – MS subjects  Left/Right – HC subjects Hip, Knee, and Ankle values were summed to determine composite symmetry measures

21. Methods – Data Analysis (HC) These trend symmetry values are on the old scale, where 0=perfect symmetry, 1=no symmetry. Couldn’t change the snapshot of a table.

22. Methods – Data Analysis (MS Fresh) These trend symmetry values are on the old scale, where 0=perfect symmetry, 100=no symmetry. Couldn’t change the snapshot of a table.

23. Methods – Data Analysis (MS Fatigued) These trend symmetry values are on the old scale, where 0=perfect symmetry, 100=no symmetry. Couldn’t change the snapshot of a table.

24. Results – MS vs. Control example HC MS

25. Results – MS and Controls MS subjects generally more asymmetrical than controls * p<0.05 These trend symmetry values are on the old scale, where 0=perfect symmetry, 100=no symmetry. Couldn’t change the snapshot of a table.

26. Results – Fresh vs. Fatigued example Fresh Fatigued

27. Results – MS Fresh and Fatigued MS subjects generally become more asymmetrical when fatigued * p<.10 These trend symmetry values are on the old scale, where 0=perfect symmetry, 100=no symmetry. Couldn’t change the snapshot of a table.

28. Results – Symmetry and EDSS No significant correlations between disease severity and changes in symmetry from fresh to fatigued conditions The trend symmetry values are on the old scale, where 0=perfect symmetry, 100=no symmetry. Couldn’t change the snapshot of a figure.

29. Conclusions MS subjects are less symmetrical than healthy control subjects MS subjects generally become less symmetrical when fatigued There was no significant correlation between disease severity and changes in symmetry measures from fresh to fatigued conditions.