Game Fun Prediction Based on Frequency Domain Physiological Signals: Observational Study
Traditionally, the subjective questionnaire collected from game players is regarded as a primary tool to evaluate a video game. However, the subjective evaluation result may vary due to individual differences, and it is not easy to provide real-time feedback to optimize the user experience. This pap...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/16/7051 |
| _version_ | 1827728734948425728 |
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| author | Yeong-Yuh Xu Chi-Huang Shih Yan-Ting You |
| author_facet | Yeong-Yuh Xu Chi-Huang Shih Yan-Ting You |
| author_sort | Yeong-Yuh Xu |
| collection | DOAJ |
| description | Traditionally, the subjective questionnaire collected from game players is regarded as a primary tool to evaluate a video game. However, the subjective evaluation result may vary due to individual differences, and it is not easy to provide real-time feedback to optimize the user experience. This paper aims to develop an objective game fun prediction system. In this system, the wearables with photoplethysmography (PPG) sensors continuously measure the heartbeat signals of game players, and the frequency domain heart rate variability (HRV) parameters can be derived from the inter-beat interval (IBI) sequence. Frequency domain HRV parameters, such as low frequency(LF), high frequency(HF), and LF/HF ratio, highly correlate with the human’s emotion and mental status. Most existing works on emotion measurement during a game adopt time domain physiological signals such as heart rate and facial electromyography (EMG). Time domain signals can be easily interfered with by noises and environmental effects. The main contributions of this paper include (1) regarding the curve transition and standard deviation of LF/HF ratio as the objective game fun indicators and (2) proposing a linear model using objective indicators for game fun score prediction. The self-built dataset in this study involves ten healthy participants, comprising 36 samples. According to the analytical results, the linear model’s mean absolute error (MAE) was 4.16%, and the root mean square error (RMSE) was 5.07%. While integrating this prediction model with wearable-based HRV measurements, the proposed system can provide a solution to improve the user experience of video games. |
| first_indexed | 2024-03-10T23:37:07Z |
| format | Article |
| id | doaj.art-f1601cb146694a12a43e3f12bb9ef107 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T23:37:07Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f1601cb146694a12a43e3f12bb9ef1072023-11-19T02:56:06ZengMDPI AGSensors1424-82202023-08-012316705110.3390/s23167051Game Fun Prediction Based on Frequency Domain Physiological Signals: Observational StudyYeong-Yuh Xu0Chi-Huang Shih1Yan-Ting You2Department of Artificial Intelligence and Computer Engineering, National Chin-Yi University of Technology, Taichung 411, TaiwanDepartment of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 411, TaiwanDepartment of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 411, TaiwanTraditionally, the subjective questionnaire collected from game players is regarded as a primary tool to evaluate a video game. However, the subjective evaluation result may vary due to individual differences, and it is not easy to provide real-time feedback to optimize the user experience. This paper aims to develop an objective game fun prediction system. In this system, the wearables with photoplethysmography (PPG) sensors continuously measure the heartbeat signals of game players, and the frequency domain heart rate variability (HRV) parameters can be derived from the inter-beat interval (IBI) sequence. Frequency domain HRV parameters, such as low frequency(LF), high frequency(HF), and LF/HF ratio, highly correlate with the human’s emotion and mental status. Most existing works on emotion measurement during a game adopt time domain physiological signals such as heart rate and facial electromyography (EMG). Time domain signals can be easily interfered with by noises and environmental effects. The main contributions of this paper include (1) regarding the curve transition and standard deviation of LF/HF ratio as the objective game fun indicators and (2) proposing a linear model using objective indicators for game fun score prediction. The self-built dataset in this study involves ten healthy participants, comprising 36 samples. According to the analytical results, the linear model’s mean absolute error (MAE) was 4.16%, and the root mean square error (RMSE) was 5.07%. While integrating this prediction model with wearable-based HRV measurements, the proposed system can provide a solution to improve the user experience of video games.https://www.mdpi.com/1424-8220/23/16/7051video gamephotoplethysmographyheart rate variability |
| spellingShingle | Yeong-Yuh Xu Chi-Huang Shih Yan-Ting You Game Fun Prediction Based on Frequency Domain Physiological Signals: Observational Study Sensors video game photoplethysmography heart rate variability |
| title | Game Fun Prediction Based on Frequency Domain Physiological Signals: Observational Study |
| title_full | Game Fun Prediction Based on Frequency Domain Physiological Signals: Observational Study |
| title_fullStr | Game Fun Prediction Based on Frequency Domain Physiological Signals: Observational Study |
| title_full_unstemmed | Game Fun Prediction Based on Frequency Domain Physiological Signals: Observational Study |
| title_short | Game Fun Prediction Based on Frequency Domain Physiological Signals: Observational Study |
| title_sort | game fun prediction based on frequency domain physiological signals observational study |
| topic | video game photoplethysmography heart rate variability |
| url | https://www.mdpi.com/1424-8220/23/16/7051 |
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