The application of machine-learning on lower limb motion analysis in human exoskeleton system

Authors:
Cao-yuan Zhao;Xiang-gang Zhang;Qing Guo
Affiliations:
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, PRC;School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, PRC;School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, PRC
Venue:
ICSR'12 Proceedings of the 4th international conference on Social Robotics
Year:
2012

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Abstract

This article briefly describes the research status of the human exoskeleton system, and gives a summary of the human lower limb gait analysis. On this basis, the machine learning classification algorithms and clustering algorithms are used for offline data mining on a data collection of prototype, in order to establish a classifier for movement prediction and judgment of lower limb. At the same time, the article analyses the procedure of standing from sitting utilizing clustering algorithm for rehabilitation exoskeleton. Experiments mainly refer to the C4.5 decision tree algorithm, Bayesian classification algorithm and clustering algorithm EM. Weka software simulation results show that the lower limb motion can be judged by the classifier making use of gait analysis data accurately, and the lower limb motion for different users at different time and different environments could be clustered by cluster. Effectively using of these results can offer great convenience to the flexibility control for exoskeleton so that exoskeleton system could achieve human-computer coupling.