Date of Graduation
8-2017
Document Type
Thesis
Degree Name
Master of Science in Electrical Engineering (MSEE)
Degree Level
Graduate
Department
Electrical Engineering
Advisor/Mentor
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.
Citation
Sun, S. (2017). Statistical Movement Classification based on Hilbert-Huang Transform. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/2476