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Cluster-based analysis for personalized stress evaluation using physiological signals.

Abstract：

:Technology development in wearable sensors and biosignal processing has made it possible to detect human stress from the physiological features. However, the intersubject difference in stress responses presents a major challenge for reliable and accurate stress estimation. This research proposes a novel cluster-based analysis method to measure perceived stress using physiological signals, which accounts for the intersubject differences. The physiological data are collected when human subjects undergo a series of task-rest cycles, incurring varying levels of stress that is indicated by an index of the State Trait Anxiety Inventory. Next, a quantitative measurement of stress is developed by analyzing the physiological features in two steps: 1) a k -means clustering process to divide subjects into different categories (clusters), and 2) cluster-wise stress evaluation using the general regression neural network. Experimental results show a significant improvement in evaluation accuracy as compared to traditional methods without clustering. The proposed method is useful in developing intelligent, personalized products for human stress management.

journal_name

IEEE J Biomed Health Inform

journal_title

IEEE journal of biomedical and health informatics

authors

Xu Q,Nwe TL,Guan C

doi

10.1109/JBHI.2014.2311044

subject

Has Abstract

pub_date

2015-01-01 00:00:00

pages

275-81

issue

1

eissn

2168-2194

issn

2168-2208

journal_volume

19

pub_type

杂志文章,随机对照试验

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