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Game Fun Prediction Based on Frequency Domain Physiological Signals: Observational Study.

Yeong-Yuh Xu¹, Chi-Huang Shih², Yan-Ting You²

¹Department of Artificial Intelligence and Computer Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan.

Sensors (Basel, Switzerland)

|August 26, 2023

Summary

This study introduces an objective system using wearable sensors to predict video game fun by analyzing heart rate variability (HRV). The system offers real-time feedback for enhanced player experience, moving beyond subjective questionnaires.

Keywords:

heart rate variability photoplethysmography video game

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Area of Science:

Human-Computer Interaction
Biomedical Engineering
Game Design

Background:

Traditional video game evaluation relies on subjective questionnaires, which have limitations like individual variability and lack of real-time feedback.
Objective physiological measures offer a more consistent and immediate approach to assessing player experience.

Purpose of the Study:

To develop an objective system for predicting game fun using physiological signals.
To establish objective indicators for game fun based on heart rate variability (HRV).

Main Methods:

Utilized photoplethysmography (PPG) sensors in wearables to continuously measure heartbeat signals and derive inter-beat interval (IBI) sequences.
Analyzed frequency domain heart rate variability (HRV) parameters, specifically the low frequency (LF) and high frequency (HF) components and their ratio (LF/HF).
Developed a linear model incorporating the curve transition and standard deviation of the LF/HF ratio as objective game fun indicators.

Main Results:

The proposed linear model achieved a mean absolute error (MAE) of 4.16% and a root mean square error (RMSE) of 5.07% in predicting game fun scores.
Identified the curve transition and standard deviation of the LF/HF ratio as significant objective indicators for game fun.
Demonstrated the feasibility of integrating wearable-based HRV measurements with a predictive model.

Conclusions:

The developed objective system provides a reliable method for predicting game fun, overcoming limitations of subjective evaluations.
The system offers potential for real-time feedback to optimize the user experience in video games.
This research highlights the utility of HRV analysis for objective assessment of player engagement and enjoyment.