JRM Vol.28 No.3 pp. 418-424
doi: 10.20965/jrm.2016.p0418


Gait and Posture Analysis Method Based on Genetic Algorithm and Support Vector Machines with Acceleration Data

Huan Gou, Tengda Shi, Lei Yan, and Jiang Xiao

School of Technology, Beijing Forestry University
No.35 Qinghua East Road, Haidian District, Beijing 100083, China

Corresponding author

May 26, 2015
April 4, 2016
June 20, 2016
gait, posture, SVM, analysis, acceleration
The support vector machine (SVM) we propose for automated gait and posture recognition is based on acceleration. Acceleration data are obtained from four accelerators attached to the human thigh and lower leg. In the experiment, volunteers take part in four gaits and postures, i.e., sitting, standing, walking and ascending stairs. Acceleration data that are preprocessed include normalization, a wavelet filter and dimension reduction. We used the SVM and a neural network to analyze the data processed. Simulation results indicate that SVM parameters C and g selected by a genetic algorithm (GA) are more effective for gait and posture analysis when compared to the parameter C and g selected by a grid search. The overall classification precision of the four gaits and postures exceeds 90.0%, and neural network simulation results indicate that the SVM using the GA is preferable for use in analysis.
The result of parameters optimization by GA

The result of parameters optimization by GA

Cite this article as:
H. Gou, T. Shi, L. Yan, and J. Xiao, “Gait and Posture Analysis Method Based on Genetic Algorithm and Support Vector Machines with Acceleration Data,” J. Robot. Mechatron., Vol.28 No.3, pp. 418-424, 2016.
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