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

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

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

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

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

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

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

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

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

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

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

Genetic Algorithm Feasibility Impact Significance Conclusion

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

Questions and answers