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Vol. 34, No. 8(3), S&M3042

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Vol. 32, No. 8(2), S&M2292

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Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials, Volume 29, Number 7(2) (2017)
Copyright(C) MYU K.K.
pp. 1081-1088
S&M1401 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2017.1592
Published: July 26, 2017

Detection Method of Human Three-Dimensional Body Center of Gravity Based on Inclinometer Network [PDF]

Jin-Zhuang Xiao, Zhi-Fang Yang, Hong-Rui Wang, and Xin-Cai Yang

(Received April 1, 2016; Accepted June 19, 2017)

Keywords: center of gravity, inclinometer network, motion capture system, segmental kinematic method, balance function

Research on the center of gravity (COG) of the body has wide applications in academic, medical, and other related fields. However, common devices are bulky, expensive, or inconvenient. In addition, most devices detect a two-dimensional center of mass, not a three-dimensional (3D) one. In this research, we proposed a detecting method of 3D COG. Consideration of body balance processes was realized by the rotation of each joint, and the processes could be detected by an inclinometer network installed on each main joint. On the basis of data from this network, we deduced a 3D-coordinate formula for the human COG during a tilted state using an analytical method and coordinate transformation. Then, the 3D coordinates of the COG were obtained by substituting tilt angles into the formula. Twenty subjects participated in the test. Compared with the results from a commercial motion capturing system, the standard deviation (SD) was in the range of 0.10–0.60 cm, which validates the accuracy of the proposed system. The system is convenient, economical, and requires fewer calculations.

Corresponding author: Jin-Zhuang Xiao


Cite this article
Jin-Zhuang Xiao, Zhi-Fang Yang, Hong-Rui Wang, and Xin-Cai Yang, Detection Method of Human Three-Dimensional Body Center of Gravity Based on Inclinometer Network, Sens. Mater., Vol. 29, No. 7, 2017, p. 1081-1088.



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