Indexing cognitive load using blood volume pulse features

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

doi:10.1145/3027063.3053140

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 proceeding › Conference proceeding contribution › peer-review

8 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 language	English
Title of host publication	Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems
Subtitle of host publication	Explore, Innovate, Inspire
Place of Publication	New York
Publisher	Association for Computing Machinery (ACM)
Pages	2269-2275
Number of pages	7
ISBN (Electronic)	9781450346566
DOIs	https://doi.org/10.1145/3027063.3053140
Publication status	Published - 2017
Externally published	Yes
Event	35th Annual Conference on Human Factors in Computing Systems, CHI 2017 - Denver, United States Duration: 6 May 2017 → 11 May 2017

Conference

Conference	35th Annual Conference on Human Factors in Computing Systems, CHI 2017
Country/Territory	United States
City	Denver
Period	6/05/17 → 11/05/17

Keywords

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

Access to Document

10.1145/3027063.3053140

Cite this

@inproceedings{c1112935890b4808b63534b536d9520f,

title = "Indexing cognitive load using blood volume pulse features",

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.",

keywords = "BVP, cognitive load, peak and max feratures, Cognitive load, Peak and max features",

author = "Jianlong Zhou and Arshad, {Syed Z.} and Simon Luo and Kun Yu and Shlomo Berkovsky and Fang Chen",

year = "2017",

doi = "10.1145/3027063.3053140",

language = "English",

pages = "2269--2275",

booktitle = "Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems",

publisher = "Association for Computing Machinery (ACM)",

address = "United States",

note = "35th Annual Conference on Human Factors in Computing Systems, CHI 2017 ; Conference date: 06-05-2017 Through 11-05-2017",

}

Zhou, J, Arshad, SZ, Luo, S, Yu, K, Berkovsky, S & Chen, F 2017, Indexing cognitive load using blood volume pulse features. in Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems: Explore, Innovate, Inspire. Association for Computing Machinery (ACM), New York, pp. 2269-2275, 35th Annual Conference on Human Factors in Computing Systems, CHI 2017, Denver, Colorado, United States, 6/05/17. https://doi.org/10.1145/3027063.3053140

Indexing cognitive load using blood volume pulse features. / Zhou, Jianlong; Arshad, Syed Z.; Luo, Simon et al.
Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems: Explore, Innovate, Inspire. New York: Association for Computing Machinery (ACM), 2017. p. 2269-2275.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Indexing cognitive load using blood volume pulse features

AU - Zhou, Jianlong

AU - Arshad, Syed Z.

AU - Luo, Simon

AU - Yu, Kun

AU - Berkovsky, Shlomo

AU - Chen, Fang

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - BVP

KW - cognitive load

KW - peak and max feratures

KW - Cognitive load

KW - Peak and max features

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DO - 10.1145/3027063.3053140

M3 - Conference proceeding contribution

SP - 2269

EP - 2275

BT - Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems

PB - Association for Computing Machinery (ACM)

CY - New York

T2 - 35th Annual Conference on Human Factors in Computing Systems, CHI 2017

Y2 - 6 May 2017 through 11 May 2017

ER -

Indexing cognitive load using blood volume pulse features

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Keywords

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