Key result
A linear model using PPG-derived frequency domain HRV parameters (LF/HF ratio) predicted subjective game fun scores with a mean absolute error of 4.16%.
Why the study?
Subjective questionnaires to evaluate video games vary by individual and lack real-time feedback, while existing time-domain physiological measures are susceptible to noise.
Observational (n=10)
Effect estimate: MAE 4.16%
Frequency domain HRV parameters derived from wearable PPG sensors can predict subjective video game fun scores with relatively low error.
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.
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Xu et al. (2023) conducted an observational in Healthy participants (n=10). Frequency domain HRV parameters (LF/HF ratio) via wearable PPG was evaluated on Mean absolute error (MAE) of the linear prediction model for game fun scores (MAE 4.16%). A linear model using PPG-derived frequency domain HRV parameters (LF/HF ratio) predicted subjective game fun scores with a mean absolute error of 4.16%.
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