Serious games are receiving increasing attention in the field of cultural heritage (CH) applications. A special field of CH and education is intangible cultural heritage and particularly dance. Machine learning (ML) tools are necessary elements for the success of a serious game platform since they introduce intelligence in processing and analysis of users’ interactivity. ML provides intelligent scoring and monitoring capabilities of the user's progress in a serious game platform. In this article, we introduce a deep learning model for motion primitive classification. The model combines a convolutional processing layer with a bidirectional analysis module. This way, RGB information is efficiently handled by the hierarchies of convolutions, while the bidirectional properties of a long short term memory (LSTM) model are retained. The resulting convolutionally enhanced bidirectional LSTM (CEBi-LSTM) architecture is less sensitive to skeleton errors, occurring using low-cost sensors, such as Kinect, while simultaneously handling the high amount of detail when using RGB visual information.

中文翻译：

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使用深度学习模型对严肃游戏平台进行运动原语分类

严肃游戏在文化遗产（CH）应用领域受到越来越多的关注。CH和教育的一个特殊领域是非物质文化遗产，尤其是舞蹈。机器学习 (ML) 工具是严肃游戏平台成功的必要元素，因为它们在处理和分析用户交互性方面引入了智能。ML 在严肃的游戏平台中提供了对用户进度的智能评分和监控功能。在本文中，我们介绍了一种用于运动原语分类的深度学习模型。该模型结合了卷积处理层和双向分析模块。这样，RGB 信息由卷积的层次结构有效处理，同时保留了长短期记忆 (LSTM) 模型的双向特性。