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)
Human identification by gait analysis
Proceedings of the 2nd International Workshop on Systems and Networking Support for Health Care and Assisted Living Environments
Automatic gait recognition based on statistical shape analysis
IEEE Transactions on Image Processing
A study on human gait analysis
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
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This paper describes gait identification using cumulants of accelerometer data. Accelerometer data of three different walking speeds for each subject (normal, slow and fast) was acquired by a cell phone placed on the person's hip. Data analysis was based on gait cycles that were detected first. Cumulants of order from 1 to 4 with lags from 0 to 10 for second, third and fourth order cumulants were calculated from the cycles and used as feature vectors for classification which was accomplished by support vector machines (SVM). Six healthy young subjects participated in the experiment. According to their gait classification the average recognition rate was 93.1%. A similarity measure for discerning different walking types of the same subject was also introduced using principal component analysis (PCA).