Emotion recognition from physiological signals using wireless sensors for presence technologies

Nasoz, Fatma; Alvarez, Kaye; Lisetti, L.; Finkelstein, Neal

doi:10.1007/s10111-003-0143-x

Cited by 285 publications

(136 citation statements)

References 42 publications

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“…The findings from this study were consistent with previous research that established strong physiologic connections between emotional expression and physiological arousal (e.g., skin conductance, temperature, respiration, blood flow) (Nasoz et al, 2004;Picard, 2001). …”

Section: W81xwh-07-2-0086supporting

confidence: 91%

Transformative Learning: Patterns of Psychophysiologic Response and Technology-Enabled Learning and Intervention Systems

Jerome¹,

Jordan²,

Rodericks³

2008

PsycEXTRA Dataset

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS.

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Section: W81xwh-07-2-0086supporting

confidence: 91%

Transformative Learning: Patterns of Psychophysiologic Response and Technology-Enabled Learning and Intervention Systems

Jerome¹,

Jordan²,

Rodericks³

2008

PsycEXTRA Dataset

View full text Add to dashboard Cite

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“…Mood induction procedures are widely used in various studies like Kirchsteiger et al (2006) and Nasoz et al (2003). Four movie clips represented the four quadrants of two-dimensional (valence, arousal) model with one movie clip selected as neutral condition.…”

Section: Methodsmentioning

confidence: 99%

Do moods affect programmers’ debug performance?

2010

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There is much research that shows people's mood can affect their activities. This paper argues that this also applies to programmers, especially their debugging. Literature-based framework is presented linking programming with various cognitive activities as well as linking cognitive activities with moods. Further, the effect of mood on debugging was tested in two experiments. In the first experiment, programmers (n = 72) saw short movie clips selected for their ability to provoke specific moods. Afterward, they completed a debugging test. Results showed the video clips had a significant effect on programmers' debugging performance; especially, there was a significant difference after watching low-and high-arousalevoking video clips. In the second experiment, programmers' mood was manipulated by asking participants (n = 19) to dry run algorithms for at least 16 min. They performed some physical exercises before continuing dry running algorithms again. The results showed a significant increase in arousal and valence that coincided with an improvement in programmers' task performance after the physical exercises. Together, this suggests that programmers' moods influence some programming tasks such as debugging.

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“…In this paper we do not focus on linear psychophysiological relationships but, instead, we apply machine learning to create non-linear models that approximate the function between a set of physiological signal attributes and self-reported affective states. While most studies in machine learning within psychophysiology ( [17,18,7,19] among others) focus on the classification accuracies of different methods and disregard the particular models built, this paper analyses the effect of various physiological features in the prediction of affective states.…”

Section: Related Workmentioning

confidence: 99%

Generic Physiological Features as Predictors of Player Experience

Martínez

Garbarino

Yannakakis

2011

Affective Computing and Intelligent Interaction

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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 selfreported 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.

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Emotion recognition from physiological signals using wireless sensors for presence technologies

Cited by 285 publications

References 42 publications

Transformative Learning: Patterns of Psychophysiologic Response and Technology-Enabled Learning and Intervention Systems

Transformative Learning: Patterns of Psychophysiologic Response and Technology-Enabled Learning and Intervention Systems

Do moods affect programmers’ debug performance?

Generic Physiological Features as Predictors of Player Experience

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