The Korea Institute of Information and Commucation Engineering 2016; 14(1): 45-50
Published online March 31, 2016
https://doi.org/10.6109/jicce.2016.14.1.045
© Korea Institute of Information and Communication Engineering
Gait-based classification has gained much interest as a possible authentication method because it incorporate an intrinsic personal signature that is difficult to mimic. The study investigates machine learning techniques to mitigate the natural variations in gait among different subjects. We incorporated several machine learning algorithms into this study using the data mining package called Waikato Environment for Knowledge Analysis (WEKA). WEKA's convenient interface enabled us to apply various sets of machine learning algorithms to understand whether each algorithm can capture certain distinctive gait features. First, we defined 24 gait features by analyzing three-axis acceleration data, and then selectively used them for distinguishing subjects 10 years of age or younger from those aged 20 to 40. We also applied a machine learning voting scheme to improve the accuracy of the classification. The classification accuracy of the proposed system was about 81% on average.
Keywords Classification, Gait analysis, Machine learning algorithm, WEKA
The Korea Institute of Information and Commucation Engineering 2016; 14(1): 45-50
Published online March 31, 2016 https://doi.org/10.6109/jicce.2016.14.1.045
Copyright © Korea Institute of Information and Communication Engineering.
Ik-Hyun Youn*, Kwanghee Won, Jong-Hoon Youn, and Jeremy Scheffler
University of Nebraska at Omaha,Pius X High School
Gait-based classification has gained much interest as a possible authentication method because it incorporate an intrinsic personal signature that is difficult to mimic. The study investigates machine learning techniques to mitigate the natural variations in gait among different subjects. We incorporated several machine learning algorithms into this study using the data mining package called Waikato Environment for Knowledge Analysis (WEKA). WEKA's convenient interface enabled us to apply various sets of machine learning algorithms to understand whether each algorithm can capture certain distinctive gait features. First, we defined 24 gait features by analyzing three-axis acceleration data, and then selectively used them for distinguishing subjects 10 years of age or younger from those aged 20 to 40. We also applied a machine learning voting scheme to improve the accuracy of the classification. The classification accuracy of the proposed system was about 81% on average.
Keywords: Classification, Gait analysis, Machine learning algorithm, WEKA
Ansary Shafew, Dongwan Kim, and Daehee Kim
Journal of information and communication convergence engineering 2024; 22(4): 303-309 https://doi.org/10.56977/jicce.2024.22.4.303Taejun Lee, Hakseong Kim, and Hoekyung Jung, Member, KIICE
Journal of information and communication convergence engineering 2023; 21(2): 110-116 https://doi.org/10.56977/jicce.2023.21.2.110Phuoc-Hai Huynh, Van Hoa Nguyen, and Thanh-Nghi Do
Journal of information and communication convergence engineering 2019; 17(1): 14-20 https://doi.org/10.6109/jicce.2019.17.1.14