Title:

GS6-5 Proposal and Evaluation of the Gait Classification Method Using Arm Acceleration Data and Decision Tree

Publication: ICAROB2017
Volume: 22
Pages: 104-107
ISSN: 2188-7829
DOI: 10.5954/ICAROB.2017.GS6-5
Author(s): Kodai Kitagawa, Yu Taguchi, Nobuyuki Toya
Publication Date: January 19, 2017
Keywords: Gait, Falling prevention, Decision tree, Arm, Acceleration, Smartphone
Abstract: We have been developing a system for falling prevention to classify gait patterns based on stride length and foot clearance by arm accelerations. In this paper, we propose gait classification method using arm acceleration data and decision tree. Also, we evaluate whether decision tree using three-axis accelerations as feature quantities could classify three gait patterns. (Three gait patterns are "Normal", "High step" and "Long step".) The result showed that this method can classify three gait patterns of some subjects.
PDF File: https://alife-robotics.co.jp/members2017/icarob/data/html/data/GS_pdf/GS6/GS6-5.pdf
Copyright: © The authors.
This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited.
See for details: https://creativecommons.org/licenses/by-nc/4.0/

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