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NEURO-FUZZY SYSTEM FOR DETECTING PD PATIENTS BASED ON EUCLID DISTANCE, FFT, AND PCA
Journal of Mechanics in Medicine and Biology ( IF 0.8 ) Pub Date : 2020-09-18 , DOI: 10.1142/s0219519420400175
SEOK-WOO JANG 1 , SANG-HONG LEE 2
Affiliation  

This study proposes a method to distinguish between healthy people and Parkinson’s disease patients using sole pressure sensor data, neural network with weighted fuzzy membership (NEWFM), and preprocessing techniques. The preprocessing techniques include fast Fourier transform (FFT), Euclidean distance, and principal component analysis (PCA), to remove noise in the data for performance enhancement. To make the features usable as inputs for NEWFM, the Euclidean distances between the left and right sole pressure sensor data were used at the first step. In the second step, the frequency scales of the Euclidean distances extracted in the first step were divided into individual scales by the FFT using the Hamming method. In the final step, 1–15 dimensions were extracted as the features of NEWFM from the individual scales by the FFT extracted in the second step by the PCA. An accuracy of 75.90% was acquired from the eight dimensions as the inputs of NEWFM.

中文翻译:

基于欧几里得距离、FFT 和 PCA 检测 PD 患者的神经模糊系统

本研究提出了一种使用单一压力传感器数据、具有加权模糊隶属度的神经网络 (NEWFM) 和预处理技术来区分健康人和帕金森病患者的方法。预处理技术包括快速傅里叶变换 (FFT)、欧几里德距离和主成分分析 (PCA),以去除数据中的噪声以提高性能。为了使这些特征可用作 NEWFM 的输入,第一步使用左右鞋底压力传感器数据之间的欧几里德距离。第二步,将第一步中提取的欧几里得距离的频率尺度通过 FFT 使用 Hamming 方法划分为单独的尺度。在最后一步,通过 PCA 在第二步中提取的 FFT,从各个尺度中提取了 1-15 个维度作为 NEWFM 的特征。作为 NEWFM 的输入,从八个维度获得了 75.90% 的准确度。
更新日期:2020-09-18
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