Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Discrete Random Signals and Statistical Signal Processing
Discrete Random Signals and Statistical Signal Processing
Gait analyzer based on a cell phone with a single three-axis accelerometer
Proceedings of the 8th conference on Human-computer interaction with mobile devices and services
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Foot Step Based Person Identification Using Histogram Similarity and Wavelet Decomposition
ISA '08 Proceedings of the 2008 International Conference on Information Security and Assurance (isa 2008)
Principal component analysis for greenhouse modelling
WSEAS TRANSACTIONS on SYSTEMS
Human identification by gait analysis
Proceedings of the 2nd International Workshop on Systems and Networking Support for Health Care and Assisted Living Environments
An advanced hybrid machine learning approach for assessment of the change of gait symmetry
WSEAS Transactions on Computers
Automatic gait recognition based on statistical shape analysis
IEEE Transactions on Image Processing
Proceedings of the 2013 international conference on Intelligent user interfaces
An Analysis of Different Approaches to Gait Recognition Using Cell Phone Based Accelerometers
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
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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 different walking types of the same subject was also introduced using dimension reduction on accelerometer data by PCA.