1. Equine Gait Analysis and Visualization Methods Dr. Marjorie Skubic Samer Arafat Justin Satterley Computer Engineering & Computer Science Dr. Kevin Keegan Veterinary Medicine & Surgery

2. Motion capture Raw data Transformed data for analysis Classification right lame left lame sound Overview Animation for visualization Database Pre-process and store

3. Motion capture Raw data Transformed data for analysis Animation for visualization Database Pre-process and store Classification Can this be applied to human motion?

4. Animation for visualization Database Pre-process and store Motion capture Raw data Transformed data for analysis Classification right lame left lame sound Analysis and Classification

5. Gait Analysis Cycle Measurement of walking biomechanics. Computation of temporal parameters, body kinematics, or EMG signals. Identification, assessment, and characterization of abnormal gait. Recommendations for treatment alternatives. Periodic analysis post intervention measures improvement.

6. Difficult Problem Wealth of information. Complexity of motion. Uncertainty about gait data quality. Mild lameness problem difficulty. Formulating a generalized method

7. Examples

8. Computerized Analysis Provides objective evaluation of interrelationships between observed body parts Signal Processing techniques: –Fourier Preprocessing Fixed frequency window not suited for short duration pulsation Few harmonics represent signal details Produces no time domain localization –Discrete Wavelet Preprocessing Limited window (scale) widths, at 1,2,4,8,16,32,… Limited on time localization. –Continuous Wavelet Preprocessing data collection preprocessing classification

9. Fourier Preprocessing Holzreiter1993 and Lakany1997 showed good results for the 2-class problem: sound vs. lame gait. Fourier Analysis: although localized in frequency domain, fixed frequency window not suited for short duration pulsation; few harmonics represent signal details; produces no time domain localization. Lakany2000 concluded that wavelet transform has the advantage of extracting local or global features.

10. Discrete Wavelet Preprocessing Marguitu1997, Verdini2000, and Sekine2000 showed good results for the 2-class problem. DWT has limited window (scale) widths, at 1,2,4,8,16,32,… DWT is limited on time localization.

11. Continuous Wavelet Preprocessing Lakany 2000 showed good results for the 2-class gait problem: sound vs. lame. CWT has temporal localization. Has flexible window sizes. Is translation invariant. Can be used to extract generic features: local and global signal characteristics.

12. CWT Coefficients CWT may be thought of as a rough measure of similarity between wavelet and signal segment. Need to select wavelet most similar to signal characteristics. Example Wavelets

13. Wavelet Selection Standard method is to: 1. Do a visual inspection of signal characteristics and available wavelets. 2. Select a wavelet that “looks” similar to dominant signal characteristics. Examples: Aminian 2002, Ismail1 998, Lakany 2000. Method is subjective, time-consuming, manual, and imprecise (most similar, or best, wavelet might not get selected).

14. Automatic Wavelet Selection Need a method that searches for a wavelet that is maximally similar to signal characteristics. Analyze information content of transformed signals. System’s self-information is related to uncertainty [Shannon 1949]. Maximum entropy yields highest self-information.

15. Uncertainty Types Complex information systems exhibit several types of uncertainty [Pal2000], [Yager2000]. Include - Probabilistic: uncertainty due to randomness. - Fuzzy: measures average ambiguity in fuzzy sets. - Non-specific: ambiguity in specifying exact solution.

16. Combined Uncertainty Shannon 1949 introduced maximum entropy, which is a probabilistic uncertainty measure. We explore a generalization that includes fuzzy and probabilistic uncertainties. Fuzzy and probabilistic uncertainties are combined together in order to compute maximum uncertainty. Better models system self-information.

17. Best Wavelet Selection Select an initial set of scales: 16,32,52,64. For each scale value, For each Horse data set, For each available wavelet Compute CWT Compute Coefficient’s Uncertainty Horse’s B.W. has Maximum Uncertainty Best Wavelet is selected most often by Horses.

18. Best Transformation Analysis

19. Time Sequence Process TS process combines together 3, 5, or 7 adjacent transformed signal data points. Captures intra-signal variation over time. Composition captures temporal trend. TS points form feature vectors that are input to NN. Helps NN combine together multiple signals in order to capture their temporal correlations.

20. Forming Feature Vectors for a Neural Network Classifier

21. Gait Classification Experiments Navicular data set: used 8 horses/class. Used BP neural nets for training with conjugate gradient algorithm. Used 6-fold for training, 2-fold for testing. Correct classification percentage (CCP) computed 8 experiments make 1 round. 7 rounds total. Median CCP is recorded.

22. Navicular Set Results

23. Induced-Lameness Set Special shoes attached to reasonably sound horses in order to induce a level of lameness on a certain side. Two level sets: mild and severe. Severe used here. Navicular set trained NN used to pick good horses. 9 horses determined to be good on all 3 classes. BWS with Combined Uncertainty picked best wavelets. 95% CCP recorded.

24. Fetlock, Elbow, & Carpus 2 points suggested by medical practitioners to pick side of lameness: poll and foot. Multiple features extracted per signal. Single features scored low CCP. Multiple features improved performance (83% CCP). Poll and foot needed only one feature. Poll + one leg point can pick side of lameness. Foot is best point.

25. Small Feature Extraction Used BWS with CU to extract foot’s small feature. Computed 87% CCP. Information in small features Zoom-in on desired features. Avoid scales < 6

26. Intermediate Conclusions BWS algorithm may be used to extract gait signal characteristics. TS process captures intra-signal trend changes. Combined Uncertainty better models system’s self- information, compared to Prob. or Fuzzy Uncertainty. BWS using CU algorithm automatically selects wavelets that are most similar to generic periodic signals. Shannon’s maximum entropy may be generalized to maximum combined uncertainty. Poll + 1 leg signal enough to characterize lameness, with the foot being the best leg point.

27. Future Plan Experiment with new key points in induced- lameness data set. Investigate other uncertainty types, like non- specificity. Evaluate methods using synthetic data. Evaluate induced-lameness data using NN trained with induced-lameness data and tested on navicular data set.

28. Animation for visualization Database Pre-process and store Motion capture Raw data Transformed data for analysis Classification right lame left lame sound Visualization Methods

29. RideHP

30. RideHP Raw Data (Pitch) Time Angular Velocity Integrated Data (Pitch) Time Position

31. RideHP Integrated Data (Pitch) Time Position Adjusted Integrated Data (Pitch) Time Position

32. RideHP Slowed (75%) Side View

33. Motion capture Raw data Transformed data for analysis Animation for visualization Database Pre-process and store Classification Can this be applied to human motion?

34. Possible Application to Human Motion Monitoring treatments for injuries and disabilities –Is the treatment working? Monitoring the elderly –Detect mobility deterioration –Start preventative exercise Monitoring movement for sports performance

35. Questions? Contact information: Email: skubicm@missouri.eduskubicm@missouri.edu Web: www.cecs.missouri.edu/~skubicwww.cecs.missouri.edu/~skubic