Real-time detection of workload changes using heart rate variability

► We detect real-time changes in mental workload using heart rate variability (HRV). ► Our novel approach models HRV in a sub-range of a Gaussian distribution. ► We tested on 45 subjects switching from a shooting game to a surveillance task. ► On an ROC curve our method shows superior performance to...

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Bibliographic Details
Published inBiomedical signal processing and control Vol. 7; no. 4; pp. 333 - 341
Main Authors Hoover, Adam, Singh, Anirud, Fishel-Brown, Stephanie, Muth, Eric
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.07.2012
Subjects
Change point detection
CUSUM
ECG
Heart rate variability
State change
Workload
ECG
CUSUM
State change
Change point detection
Heart rate variability
Workload
Online AccessGet full text
ISSN1746-8094
DOI10.1016/j.bspc.2011.07.004

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Summary:► We detect real-time changes in mental workload using heart rate variability (HRV). ► Our novel approach models HRV in a sub-range of a Gaussian distribution. ► We tested on 45 subjects switching from a shooting game to a surveillance task. ► On an ROC curve our method shows superior performance to the classic CUSUM. This work presents a novel approach to detecting real-time changes in workload using heart rate variability (HRV). We propose that for a given workload state, the values of HRV vary in a sub-range of a Gaussian distribution. We describe methods to monitor a HRV signal in real-time for change points based upon sub-Gaussian fitting. We tested our method on subjects sitting at a computer performing a low workload surveillance task and a high workload video game task. The proposed algorithm showed superior performance compared to the classic CUSUM method for detecting task changes.
ISSN:1746-8094
DOI:10.1016/j.bspc.2011.07.004