Selecting Operator's Mental Workload Features by Means of HRV Analysis

被引：0

作者：

Yang, Shaozeng ^{[1
]}

Zhang, Jianhua ^{[1
]}

Wang, Xingyu ^{[1
]}

机构：

[1] E China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

来源：

PROCEEDINGS OF THE 10TH CONFERENCE ON MAN-MACHINE-ENVIRONMENT SYSTEM ENGINEERING | 2010年

关键词：

autoregressive modeling; heart rate variability; mental workload;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

As a fundamental work in the modeling and classification of Operator Functional State (OFS), the selection of input feature set which can best characterize operators' mental workload (MWL) is very important. Previous work shows that Heart Rate Variability (HRV) could well characterize MWL. However, there are many HRV indices. To obtain a parsimonious and effective set of HRV indices as model input features, this paper analyzes 11 subjects' heart rate data by autoregressive modeling (ARM) based spectral analysis method. After computing the correlation coefficients between MWL and HRV indices, the paper selects available indices for each subject. The result shows that each selected index reflects MWL to a certain extent. It also shows that there are comparatively great individual differences among subjects.

引用

页码：12 / 16

页数：5

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Zhang, Jian-Hua ;

Wang, Xing-Yu ;

Mahfouf, M. ;

Linkens, D. A. .

BMEI 2008: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING AND INFORMATICS, VOL 1, 2008, :668-+

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