Generic Physiological Features as Predictors of Player Experience

Héctor Pérez Martínez; Maurizio Garbarino; Georgios N. Yannakakis

Generic Physiological Features as Predictors of Player Experience

Héctor Pérez Martínez, Maurizio Garbarino, Georgios N. Yannakakis

Polytechnic University of Milan

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review

Abstract

This paper examines the generality of features extracted from heart rate (HR) and skin conductance (SC) signals as predictors of self-reported player affect expressed as pairwise preferences. Artificial neural networks are trained to accurately map physiological features to expressed affect in two dissimilar and independent game surveys. The performance of the obtained affective models which are trained on one game is tested on the unseen physiological and self- reported data of the other game. Results in this early study suggest that there exist features of HR and SC such as average HR and one and two-step SC variation that are able to predict affective states across games of different genre and dissimilar game mechanics.

Original language	English
Title of host publication	ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction
Number of pages	10
Volume	1
Publisher	Springer
Publication date	2011
Pages	267-276
ISBN (Print)	978-3-642-24599-2
Publication status	Published - 2011

Keywords

Physiological signals
Heart rate variability
Skin conductance
Player affect
Cross-game prediction

Cite this

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title = "Generic Physiological Features as Predictors of Player Experience",

abstract = "This paper examines the generality of features extracted from heart rate (HR) and skin conductance (SC) signals as predictors of self-reported player affect expressed as pairwise preferences. Artificial neural networks are trained to accurately map physiological features to expressed affect in two dissimilar and independent game surveys. The performance of the obtained affective models which are trained on one game is tested on the unseen physiological and self- reported data of the other game. Results in this early study suggest that there exist features of HR and SC such as average HR and one and two-step SC variation that are able to predict affective states across games of different genre and dissimilar game mechanics.",

keywords = "Physiological signals, Heart rate variability, Skin conductance, Player affect, Cross-game prediction, Physiological signals, Heart rate variability, Skin conductance, Player affect, Cross-game prediction",

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year = "2011",

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Generic Physiological Features as Predictors of Player Experience. / Martínez, Héctor Pérez; Garbarino, Maurizio; Yannakakis, Georgios N.
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction. Vol. 1 Springer, 2011. p. 267-276.

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review

TY - GEN

T1 - Generic Physiological Features as Predictors of Player Experience

AU - Martínez, Héctor Pérez

AU - Garbarino, Maurizio

AU - Yannakakis, Georgios N.

PY - 2011

Y1 - 2011

N2 - This paper examines the generality of features extracted from heart rate (HR) and skin conductance (SC) signals as predictors of self-reported player affect expressed as pairwise preferences. Artificial neural networks are trained to accurately map physiological features to expressed affect in two dissimilar and independent game surveys. The performance of the obtained affective models which are trained on one game is tested on the unseen physiological and self- reported data of the other game. Results in this early study suggest that there exist features of HR and SC such as average HR and one and two-step SC variation that are able to predict affective states across games of different genre and dissimilar game mechanics.

AB - This paper examines the generality of features extracted from heart rate (HR) and skin conductance (SC) signals as predictors of self-reported player affect expressed as pairwise preferences. Artificial neural networks are trained to accurately map physiological features to expressed affect in two dissimilar and independent game surveys. The performance of the obtained affective models which are trained on one game is tested on the unseen physiological and self- reported data of the other game. Results in this early study suggest that there exist features of HR and SC such as average HR and one and two-step SC variation that are able to predict affective states across games of different genre and dissimilar game mechanics.

KW - Physiological signals

KW - Heart rate variability

KW - Skin conductance

KW - Player affect

KW - Cross-game prediction

KW - Physiological signals

KW - Heart rate variability

KW - Skin conductance

KW - Player affect

KW - Cross-game prediction

M3 - Article in proceedings

SN - 978-3-642-24599-2

VL - 1

SP - 267

EP - 276

BT - ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction

PB - Springer

ER -

Generic Physiological Features as Predictors of Player Experience

Abstract

Keywords

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