Computer games are widespread nowadays and enjoyed by people of all ages. But when it comes to kids, playing these games can be more than just fun, it is a way for them to develop important skills and build emotional intelligence. Facial expressions and sounds that kids produce during gameplay reflect their feelings, thoughts, and moods. In this paper, we propose a novel framework that integrates a fuzzy approach for the recognition of emotions through the analysis of audio and video data. Our focus lies within the specific context of computer games tailored for children, aiming to enhance their overall user experience. We use the FER dataset to detect facial emotions in video frames recorded from the screen during the game. For the audio emotion recognition of sounds a kid produces during the game, we use CREMA-D, TESS, RAVDESS, and Savee datasets. Next, a fuzzy inference system is used for the fusion of results. Besides this, our system can detect emotion stability and emotion diversity during gameplay, which, together with prevailing emotion report, can serve as valuable information for parents worrying about the effect of certain games on their kids. The proposed approach has shown promising results in the preliminary experiments we conducted, involving 3 different video games, namely fighting, racing, and logic games, and providing emotion-tracking results for kids in each game. Our study can contribute to the advancement of child-oriented game development, which is not only engaging but also accounts for children's cognitive and emotional states.

翻译：如今，电脑游戏已广泛普及，各年龄段人群均乐在其中。但对于儿童而言，玩这些游戏的意义远不止娱乐——它更是培养重要技能、构建情感认知的途径。儿童在游戏过程中产生的面部表情与声音，能够反映其情绪、思维及心境。本文提出一种新颖的框架，通过融合模糊方法对音视频数据进行分析以实现情感识别。研究聚焦于儿童电脑游戏这一特定场景，旨在提升其整体用户体验。我们采用FER数据集检测游戏屏幕录制视频帧中的面部表情；针对儿童游戏过程中产生的声音情感识别，则使用CREMA-D、TESS、RAVDESS及Savee数据集。随后，通过模糊推理系统对识别结果进行融合。此外，本系统还能检测游戏过程中的情感稳定性与情感多样性，这些信息结合主导情感报告，可为担忧特定游戏对儿童影响的家长提供有价值参考。在包含格斗、竞速和逻辑游戏三类不同游戏的初步实验中，该方法为每款游戏中的儿童提供了情感追踪结果，展现出良好效果。本研究有助于推动儿童导向游戏开发的发展，使其不仅能吸引儿童参与，更能兼顾其认知与情感状态。