Date of Graduation

8-2017

Document Type

Thesis

Degree Name

Master of Science in Electrical Engineering (MSEE)

Degree Level

Graduate

Department

Electrical Engineering

Advisor

Jingxian Wu

Committee Member

Zhong Chen

Second Committee Member

Chengfan Zhang

Abstract

The goal of this project is to introduce an automatic movement classification technique of

finger movement signals using Hilbert-Huang Transform (HHT). Due to the nonlinear and

nonstationary processing behavior, movement signals are analyzed with the Hilbert-Huang

Transform (HHT). The slope of auto-correlation function and mean of frequency from first

three Intrinsic Mode Functions (IMFs) was used as feature parameters for each category.

Finally, performing support vector machine (SVM) for pattern classification completes clas-

sifying types of finger movement. According to the records of 669 trial samples of two types

of finger movement signals (thumb and pinky), average accuracy is 93.28%. In another case

of movement (thumb and pinky), average accuracy is 100%. All in all, the feature extraction

method based on Hilbert-Huang transform (HHT) can be used to achieve effective movement

classification.

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