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A Predictive Model for Emotion Recognition Based on Individual Characteristics and Autonomic Changes.

Ateke Goshvarpour^1,2, Atefeh Goshvarpour³, Ataollah Abbasi³

¹Department of Biomedical Engineering, Faculty of Electrical Engineering, Imam Reza International University, Mashhad, Razavi Khorasan, Iran.

Basic and Clinical Neuroscience

|December 2, 2022

Summary

Individual characteristics like sleep and linguality significantly influence Heart Rate Variability (HRV) changes during emotional states. The study introduces a predictive model for emotion recognition using these personal factors.

Keywords:

Emotion Heart rate Individual differences Model

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Area of Science:

Psychophysiology
Affective Computing
Biomedical Engineering

Background:

Individual differences are crucial for understanding emotion recognition.
Heart Rate Variability (HRV) is a key physiological indicator linked to emotional states and autonomic nervous system (ANS) activity.
Previous research has explored factors like age, gender, and sleep in relation to HRV, but their combined role in emotion prediction is less understood.

Purpose of the Study:

To predict changes in Heart Rate Variability (HRV) in response to affective stimuli based on individual characteristics.
To identify the optimal combinations of individual variables (age, gender, linguality, sleep) for estimating emotional HRV.
To propose a novel predictive model for emotion recognition utilizing these individual differences.

Main Methods:

Analyzed HRV indices of 47 college students exposed to images depicting happiness, sadness, fear, and relaxation.
Developed a predictive model based on regression equations to correlate individual characteristics with HRV changes.
Explored various combinations of individual variables (age, gender, linguality, sleep) to determine their predictive power.

Main Results:

Different emotional stimuli necessitate distinct combinations of individual variables for accurate HRV prediction.
Optimal variable combinations identified include LS, GL, GA, ALS, and GALS, though these varied per individual.
A significant association was observed between gender and physiological changes during emotional states.

Conclusions:

The proposed simple predictive model offers valuable insights into emotion studies by incorporating subject characteristics and autonomic parameters.
This approach highlights the importance of individual differences in the physiological responses to emotions.
The findings suggest that personalized models considering factors like sleep and linguality can improve emotion recognition accuracy.