Author : Ramadan Moawad,Hadeer Tawfik
CoAuthors : nashwa.elbendary
Source : 2020 International Conference on Innovative Trends in Communication and Computer Engineering (ITCE)
Date of Publication : 02/2020
Abstract :
—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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