Boxplots of the best subject-dependent correlations in the intensity prediction.

Boxplots of the best subject-dependent correlations in the intensity prediction.

<p>Results for both NARX models (left) and sliding-window linear regression (right) for Happiness/Joy (Q1), Disappointment/Regret (Q2), and Worry/Fear (Q3).</p>

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Bibliographic Details
Main Author:	Isabel Barradas (20614853) (author)
Other Authors:	Reinhard Tschiesner (20614856) (author), Angelika Peer (5194628) (author)
Published:	2025
Subjects:	Genetics Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified window linear regression today &# 8217 three distinct qualities scherer &# 8217 outperform traditional sliding large temporal contribution component process model perceived emotion intensity estimate emotion intensity extracted physiological features cpm ), represent cpm &# 8217 capture emotion dynamics emotion recognition research physiological dynamics emotion processes physiological signals varying intensities using data train intrasubject subjective feeling robust basis real time participants assess genetic algorithm emotional processes elegant framework dynamic nature categorised dimensions bringing insights appraisal theory
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