@article{ref1,
title="Fall risk factors analysis based on sample entropy of plantar kinematic signal during stance phase",
journal="Conference proceedings - IEEE engineering in medicine and biology society",
year="2016",
author="Liang, Shengyun and Jia, Huiyu and Li, Zilong and Li, Huiqi and Gao, Xing and Ma, Zuchang and Ma, Yingnan and Zhao, Guoru and Shengyun Liang,  and Huiyu Jia,  and Zilong Li,  and Huiqi Li,  and Xing Gao,  and Zuchang Ma,  and Yingnan Ma,  and Guoru Zhao,  and Liang, Shengyun and Li, Huiqi and Jia, Huiyu and Gao, Xing and Ma, Zuchang and Zhao, Guoru and Ma, Yingnan and Li, Zilong",
volume="2016",
number="",
pages="4832-4836",
abstract="Falls are a multi-causal phenomenon with a complex interaction. The aim of our research is to study the effect of multiple variables for potential risk of falls and construct an elderly fall risk assessment model based on demographics data and gait characteristics. A total of 101 subjects, whom belong to Malianwa Street, aged above 50 years old and participated in questionnaire survey. Participants were classified into three groups (high, medium and low risk group) according to the score of elderly fall risk assessment scale. In addition, the data of ground reaction force (GRF) and ground reaction moment (GRM) was record when they performed walking at comfortable state. The demographic variables, sample entropy of GRF and GRM, and impulse difference of bilateral foot were considered as potential explanatory variables of risk assessment model. Firstly, we investigated whether different groups could present difference in every variable. Statistical differences were found for the following variables: age (p=2.28e-05); impulse difference (p=0.02036); sample entropy of GRF in vertical direction (p=0.0144); sample entropy of GRM in anterior-posterior direction (p=0.0387). Finally, the multiple regression analysis results indicated that age, impulse difference and sample entropy of resultant GRM could identify individuals who had different levels of fall risk. Therefore, those results could potentially be useful in the fall risk assessment and monitor the state of physical function in elderly population.<p /> <p>Language: en</p>",
language="en",
issn="1557-170X",
doi="10.1109/EMBC.2016.7591809",
url="http://dx.doi.org/10.1109/EMBC.2016.7591809"
}