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Equine Gait Analysis and Visualization Methods Dr. Marjorie Skubic Samer Arafat Justin Satterley Computer Engineering & Computer Science Dr. Kevin Keegan Veterinary Medicine & Surgery. Pre-process and store. Database. Animation for visualization. Overview. Transformed data for analysis.

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Equine Gait Analysis and Visualization Methods Dr. Marjorie SkubicSamer ArafatJustin SatterleyComputer Engineering & Computer ScienceDr. Kevin KeeganVeterinary Medicine & Surgery

overview

Pre-process

and store

Database

Animation for

visualization

Overview

Transformed data for

analysis

Motion capture

Raw data

Classification

right

lame

left

lame

sound

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

Transformed data for

analysis

Motion capture

Raw data

Classification

Pre-process

and store

Database

Animation for

visualization

analysis and classification
Analysis and Classification

Transformed data for

analysis

Motion capture

Raw data

Classification

right

lame

left

lame

Pre-process

and store

sound

Database

Animation for

visualization

gait analysis cycle
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.
difficult problem
Wealth of information.

Complexity of motion.

Uncertainty about gait data quality.

Mild lameness problem difficulty.

Formulating a generalized method

Difficult Problem
computerized analysis
Computerized Analysis

data

collection

classification

preprocessing

  • 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
continuous wavelet preprocessing
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.
cwt coefficients
CWT Coefficients

Example Wavelets

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.

wavelet selection
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).

automatic wavelet selection
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.
uncertainty types
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.

combined uncertainty
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.
best wavelet selection
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.

gait classification experiments
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.
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.

Fetlock, Elbow, & Carpus
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

Small Feature Extraction
intermediate conclusions
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.
future plan
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.
visualization methods
Visualization Methods

Transformed data for

analysis

Motion capture

Raw data

Classification

right

lame

left

lame

Pre-process

and store

sound

Database

Animation for

visualization

ridehp1
RideHP

Raw Data (Pitch)

Angular

Velocity

Time

Integrated Data (Pitch)

Position

Time

ridehp2
RideHP

Integrated Data (Pitch)

Position

Time

Adjusted Integrated Data (Pitch)

Position

Time

ridehp3
RideHP

Side View

Slowed (75%)

can this be applied to human motion1
Can this be applied to human motion?

Transformed data for

analysis

Motion capture

Raw data

Classification

Pre-process

and store

Database

Animation for

visualization

possible application to human motion
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
questions
Questions?

Contact information:

Email: skubicm@missouri.edu

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