The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON SIGNAL PROCESSING
Transactions ID Number: 89-152
Full Name: Sebastijan Sprager
Position: Researcher
Age: ON
Sex: Male
Address: Smetanova ulica 17, 2000 Maribor
Country: SLOVENIA
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E-mail address: sebastijan.sprager@uni-mb.si
Other E-mails: sebastijan.sprager@gmail.com
Title of the Paper: A Cumulant-Based Method for Gait Identification Using Accelerometer Data with Principal Component Analysis and Support Vector Machine
Authors as they appear in the Paper: Sebastijan Sprager Damjan Zazula
Email addresses of all the authors: sebastijan.sprager@uni-mb.si,zazula@uni-mb.si
Number of paper pages: 10
Abstract: In this paper a cumulant-based method for identification of gait using accelerometer data is presented. Acceleration data of three different walking speeds (slow, normal and fast) for each subject was acquired by the accelerometer embedded in cell phone which was attached to the person's hip. Data analysis was based on gait cycles that were detected first. Cumulants of order from 1 to 4 with different number of lags were calculated. Feature vectors for classification were built using dimension reduction on calculated cumulants by principal component analysis (PCA). The classification was accomplished by support vector machines (SVM) with radial basis kernel. According to portion of variance covered in the calculated principal components, different lengths of feature vectors were tested. Six healthy young subjects participated in the experiment. The average person recognition rate based on gait classification was 90.3±3.2%. A similarity measure for discerning differ!
ent walking types of the same subject was also introduced using dimension reduction on accelerometer data by PCA.
Keywords: Gait Identification, Gait Recognition, Body Sensor, Accelerometer, Pattern Recognition, High-Order Statistics, Cumulants
EXTENSION of the file: .pdf
Special (Invited) Session: Gait Identification Using Cumulants of Accelerometer Data
Organizer of the Session: 639-177
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