Future University In Egypt (FUE)
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Altagamoa Al Khames, Main centre of town, end of 90th Street
New Cairo
Egypt

Hadeer Tawfik

Basic information

Name : Hadeer Tawfik
Title: TEACHING ASSISTANT
Personal Info: TEACHING ASSISTANT Hadeer Tawfik - Faculty of Computer Science

Education

Certificate Major University Year
Bachelor Computer Science Faculty of Computers & Information Technology - Future University 2013

Researches /Publications

Using Multi-Feature Fusion for Detecting Freezing of Gait Episodes in Patients with Parkinson’s Disease - 01/0

Hadeer Khalid Tawfik El Zayat

nashwa.elbendary

01/02/2020

—This paper proposes a model for detecting Freezing of Gait (FoG) episodes in patients with Parkinson’s Disease (PD) using multi-feature fusion. Theproposedapproachappliestwoschemesforfeature extraction. The first one is time-domain statistical feature engineering and the second one is spectrogrambasedtime-frequencyanalysisbyConvolutionalNeural Network (CNN) feature learning. The two extracted feature sets are fused with applying Principal Component Analysis (PCA) algorithm for dimensionality reduction. Benchmark dataset of three tri-axial accelerometer sensors for patients with PD is tested in both principle-axes and angular-axes. Moreover, performance of the proposed approach is characterized on experiments considering several Machine Learning (ML)algorithms.Experimentalresultsshowthatusing multi-feature fusion with PCA dimensionality reduction outperforms using typical single feature sets. The significance of this study is to highlight the impact of using multi-feature fusion on the performance of FoG episodes detection. Index Terms—Freezing of Gait (FoG), Parkinson’s Disease(PD),MachineLearning,ConvolutionalNeural Network (CNN), Angular-axes, Spectrogram, Principal Component Analysis (PCA), Multi-Feature Fusion

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Hand-Crafted and Learned Features Fusion for Predicting Freezing of Gait Events in Patients with Parkinson’s Disease - 01/1

Hadeer Khalid Tawfik El Zayat

Nashwa El-Bendary

01/12/2019

FreezingofGait(FoG)isacommonsymptomofParkinson’s disease (PD) that causes intermittent absence of forward progression of patient’s feet while walking. Accordingly, FoG momentary episodes are alwaysaccompaniedwithfalls.Thispaperproposesanovelmulti-feature model for predicting FoG episodes in patients with PD. The proposed approach considers FoG prediction as a multi-class classification problemwith3classes;namely,normalwalking,pre-FoG,andFoGevents.In this paper two feature extraction schemes have been applied, which are time-domain hand-crafted feature engineering and Convolutional Neural Network (CNN) based spectrogram feature learning. Also, after fusing the two extracted feature sets, Principal Component Analysis (PCA) algorithm has been deployed for dimensionality reduction. Data of three tri-axial accelerometer sensors for patients with PD, in both principleaxes and angular-axes, has been tested. Performance of the proposed approach has been characterized on experiments with respect to several Machine Learning (ML) algorithms. Experimental results have shown that using multi-feature fusion with PCA dimensionality reduction has outperformed using the other tested typical single feature sets. The significance of this study is to highlight the impact of using feature fusion of multi-feature sets on the performance of FoG episodes prediction.

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