Department of Artificial Intelligence and Computer Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan.
Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan.
Sensors (Basel). 2023 Aug 9;23(16):7051. doi: 10.3390/s23167051.
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.
传统上，从游戏玩家那里收集的主观问卷被认为是评估视频游戏的主要工具。然而，由于个体差异，主观评估结果可能会有所不同，并且不容易提供实时反馈来优化用户体验。本文旨在开发一种客观的游戏乐趣预测系统。在该系统中，带有光电容积脉搏波（PPG）传感器的可穿戴设备持续测量游戏玩家的心跳信号，并且可以从心跳间隔（IBI）序列中得出频域心率变异性（HRV）参数。频域 HRV 参数，如低频（LF）、高频（HF）和 LF/HF 比，与人类的情绪和心理状态高度相关。大多数关于游戏中情绪测量的现有工作都采用时域生理信号，如心率和面部肌电图（EMG）。时域信号很容易受到噪声和环境影响的干扰。本文的主要贡献包括：（1）将 LF/HF 比的曲线过渡和标准差作为客观游戏乐趣指标；（2）提出了一种使用客观指标进行游戏乐趣评分预测的线性模型。本研究中的自建数据集包含 10 名健康参与者，共 36 个样本。根据分析结果，线性模型的平均绝对误差（MAE）为 4.16%，均方根误差（RMSE）为 5.07%。当将该预测模型与基于可穿戴设备的 HRV 测量集成时，所提出的系统可以为改善视频游戏的用户体验提供解决方案。
