equine gait analysis and visualization methods dr. marjorie skubic samer arafat justin satterley...

Equine Gait Analysis and Equine Gait Analysis and Visualization Methods Visualization Methods

Dr. Marjorie SkubicDr. Marjorie SkubicSamer ArafatSamer Arafat

Justin SatterleyJustin Satterley

Computer Engineering & Computer ScienceComputer Engineering & Computer Science

Dr. Kevin KeeganDr. Kevin Keegan

Veterinary Medicine & SurgeryVeterinary Medicine & Surgery

Motion captureRaw data

Transformed data foranalysis

Classification

rightlame

leftlame

sound

OverviewOverview

Animation for visualization

Database

Pre-processand store




Database


Classification

Can this be applied to human Can this be applied to human motion?motion?


Database




Classification

rightlame

leftlame

sound

Analysis and ClassificationAnalysis and Classification

Gait Analysis CycleGait 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.

Difficult ProblemDifficult Problem

• Wealth of information.

• Complexity of motion.

• Uncertainty about gait data quality.

• Mild lameness problem difficulty.

• Formulating a generalized method

ExamplesExamples

Computerized AnalysisComputerized 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

datacollection preprocessing classification

Continuous Wavelet PreprocessingContinuous 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.

CWT CoefficientsCWT Coefficients

-

,s dt)t(x)t( )C(S,

)s

t(

s

1)t(,s

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

Wavelet SelectionWavelet 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).

Automatic Wavelet SelectionAutomatic 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.

)p1(log)p1(plogp)P(H kj2kjkj2j

kjkPS

Uncertainty TypesUncertainty 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.

)1(log)1(logK)A(H kj2kjkj2j

kjDTE

Combined UncertaintyCombined 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.

)A()P()P,A( pcom HHH

Best Wavelet SelectionBest 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.

Best Transformation AnalysisBest Transformation Analysis

Forming Feature Vectors Forming Feature Vectors for a Neural Network Classifierfor a Neural Network Classifier

Gait Classification ExperimentsGait 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.

Navicular Set ResultsNavicular Set Results

0

10

20

30

40

50

60

70

80

90

100

1 3 5 7

Number of TS Points

Cor

rect

Cla

ssifi

catio

n P

erce

ntag

e

Raw Data

Visual Inspection

Fuzzy or Probabilistic

Combined Uncertainty

Fetlock, Elbow, & CarpusFetlock, 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.

Small Feature ExtractionSmall 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

)A()P()P,A( pcom HHH

Intermediate ConclusionsIntermediate 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.

Future PlanFuture 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.


Database




Classification

rightlame

leftlame

sound

Visualization MethodsVisualization Methods

RideHPRideHP

RideHPRideHP

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

1 36 71 106 141 176 211 246 281 316 351 386 421 456 491 526 561 596 631 666 701 736 771 806 841 876 911 946 981

6

6.5

7

7.5

8

8.5

9

9.5

10

1 36 71 106 141 176 211 246 281 316 351 386 421 456 491 526 561 596 631 666 701 736 771 806 841 876 911 946 981

Raw Data (Pitch)

Time

AngularVelocity

Integrated Data (Pitch)

Time

Position

RideHPRideHP

6

6.5

7

7.5

8

8.5

9

9.5

10

1 36 71 106 141 176 211 246 281 316 351 386 421 456 491 526 561 596 631 666 701 736 771 806 841 876 911 946 981

Integrated Data (Pitch)

Time

Position

Adjusted Integrated Data (Pitch)

Time

Position

-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

1 27 53 79 105 131 157 183 209 235 261 287 313 339 365 391 417 443 469 495 521 547 573 599 625 651 677 703 729 755 781 807 833 859 885 911 937 963 989

RideHPRideHP

Slowed (75%) Side View




Database


Classification

Can this be applied to human Can this be applied to human motion?motion?

Possible Application to Human Possible Application to Human MotionMotion

• Monitoring treatments for injuries and disabilities– Is the treatment working?

• Monitoring the elderly– Detect mobility deterioration

– Start preventative exercise

• Monitoring movement for sports performance

Questions?Questions?

Contact information:

Email: [email protected]

Web: www.cecs.missouri.edu/~skubic

equine gait analysis and visualization methods dr. marjorie skubic samer arafat justin satterley...

Documents