Indexing cognitive load using blood volume pulse features

Jianlong Zhou, Syed Z. Arshad, Simon Luo, Kun Yu, Shlomo Berkovsky, Fang Chen

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

3 Citations (Scopus)

Abstract

Physiological responses contain rich affective information even when humans are not expressing any external signs. In this paper, we investigate the use of the Blood Volume Pulse (BVP) signals for indexing cognitive load. An experiment, which introduced cognitive load as a secondary task in a decision making context was conducted in the study. BVP signals were analyzed in order to establish relationships between BVP and cognitive load levels. A set of features (e.g. peak and max features) was found to be significantly distinctive across different cognitive load levels. The identified BVP features can be used to set up machine learning models for the automatic classification of CL levels in intelligent systems.
Original languageEnglish
Title of host publicationProceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems
Subtitle of host publicationExplore, Innovate, Inspire
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages2269-2275
Number of pages7
ISBN (Electronic)9781450346566
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event35th Annual Conference on Human Factors in Computing Systems, CHI 2017 - Denver, United States
Duration: 6 May 201711 May 2017

Conference

Conference35th Annual Conference on Human Factors in Computing Systems, CHI 2017
CountryUnited States
CityDenver
Period6/05/1711/05/17

Keywords

  • BVP
  • cognitive load
  • peak and max feratures
  • Cognitive load
  • Peak and max features

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