1. Presenter: Donovan Orn
A Comparative study for feature selection algorithms to analyze gait patterns for health purposes
Presenter: Donovan Orn

2. Motivation Widespread use of wearable devices.
Need for a systematic way to use movement patterns to access health.
Ability of wearable devices to predict potential health hazards.
Motivation

3. Goal and method Goal Method
Aid healthcare providers in the diagnosis of conditions associated with mobility impairment.
Identifying the best feature selection techniques used for processing mobility parameters used to assess health
Goal and method

4. Why Machine Learning What Why Takes in data Real time predictions
Uses pattern analysis
Predicts result
Predictions are not 100% accurate
Real time predictions
Can catch the disease early
Can be used to alert Dr. and patient
Why Machine Learning

5. Significance of feature selection
Too Few
Too Many
Not accurate
Lack of discriminating power
Overfitting
Poor performance
Over Fit Line
Fit Line
Significance of feature selection

6. The five step plan Removal and Segmentation of data Data Acquisition
Extract Features
Testing and Applying Feature Selection Techniques
Building Machine Learning Models
The five step plan

7. Introducing the data Data Shimmer R3 37 Features
Control PD
Geriatrics
Number of Subjects 10
Gender(M/F)
5:5
4:6
Age
64 ± 8.4
63.8 ± 9.3
81 ± 4.1
UPDRS III
12.7 ± 6.0
H & Y
1.7 ± .09
Shimmer R3
37 Features
UPDRS and H&Y are from early stages of disease.
Introducing the data

8. Initialize Population
Selection
Mutation
Evaluate Fitness
Final Population
Cross Over
Genetic Algorithm

9. Initialize Population
Selection
Mutation
Evaluate Fitness
Final Population
Cross Over
Size 40
Binary Set
[0,1,1,0,…,0]
4 Children Per Pair
10 Best
10 Lucky
14% Chance
Change 0 To 1
Or 1 To 0
SVM Classifier
With RBF Kernel
3-Fold Cross-
Validation
Accuracy
# Of Features
Genetic Algorithm

10. Split: Test and Training
Feature Scaling
SVM Classifier
Cross Validation
Average Accuracy
20% Test; 80% Training
RBF kernel
5 Fold Cross Validation
Fitness

11. Results With Feature Scaling Without Feature Scaling Accuracy 75.33%
#of Features 5
Features Used:
Computation Time = 58.36s
Accuracy 75.33%
#of Features 4
Features Used:
Computation Time = 58.36s
Stability
Consistency
Symmetricity
Avg_Zacc
Avg_Yvariability
Sym_StrideTime
Var_RMSZ
Sym_AccY
Stability
Symmetricity
Avg_RMSZ
CV_StrideTime
Var_RMSZ
CV_AccY
Results

12. Genetic Algorithm
Feasibility
Impact
Significance
Conclusion

13. Future works Improving the genetic algorithm
Implementing and comparing feature selection Techniques
Different and bigger datasets
Including wrist movement patterns
Future works

14. Questions and answers