Neural network-based gait assessment using measurements of a wearable sensor system

被引：0
|
作者：
Li, Guangyi [1 ]
Liu, Tao [1 ,2 ]
Li, Tong [1 ]
Inoue, Yoshio [2 ]
Yi, Jingang [3 ]
机构：
[1] Zhejiang Univ, Dept Mech Engn, State Key Lab Fluid Power Transmiss & Control, Hangzhou 310027, Zhejiang, Peoples R China
[2] Kochi Univ Technol, Sch Syst Engn, Kochi, Japan
[3] Rutgers State Univ, Dept Mech & Aerosp Engn, Piscataway, NJ 08854 USA
来源：
2014 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS IEEE-ROBIO 2014 | 2014年
关键词：
ROBOT;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A wearable gait analysis system has been developed for ambulatory measurement of gait in long-term experiments and daily life applications. Based on the measurement results of the developed system in a dynamic validation experiment, we trained neural networks for the estimation of joint angle, joint force, and joint moment using the ground reaction forces (GRFs) and moments in the gait analysis. These kinetic and kinematic parameters can be estimated through the neural networks trained especially for one person, but the possibility of building a universal model for most people should be studied in further research.
引用
收藏
页码：1673 / 1678
页数：6
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