Endowing ubiquitous robots with cognitive capabilities for recognizing emotions, sentiments, affects, and moods of humans in their context is an important challenge, which requires sophisticated and novel approaches of emotion recognition. Most studies explore data-driven pattern recognition techniques that are generally highly dependent on learning data and insufficiently effective for emotion contextual recognition. In this article, a hybrid model-based emotion contextual recognition approach for cognitive assistance services in ubiquitous environments is proposed. This model is based on: 1) a hybrid-level fusion exploiting a multilayer perceptron (MLP) neural-network model and the possibilistic logic and 2) an expressive emotional knowledge representation and reasoning model to recognize nondirectly observable emotions; this model exploits jointly the emotion upper ontology (EmUO) and the n-ary ontology of events HTemp supported by the NKRL language. For validation purposes of the proposed approach, experiments were carried out using a YouTube dataset, and in a real-world scenario dedicated to the cognitive assistance of visitors in a smart devices showroom. Results demonstrated that the proposed multimodal emotion recognition model outperforms all baseline models. The real-world scenario corroborates the effectiveness of the proposed approach in terms of emotion contextual recognition and management and in the creation of emotion-based assistance services.

中文翻译：

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用于认知辅助服务的基于混合模型的情感上下文识别

赋予无处不在的机器人以认知能力来识别人类在其环境中的情绪、情绪、情感和情绪是一项重要的挑战，这需要复杂而新颖的情绪识别方法。大多数研究探索了数据驱动的模式识别技术，这些技术通常高度依赖于学习数据，对情感上下文识别的有效性不足。在本文中，提出了一种基于混合模型的情感上下文识别方法，用于无处不在的环境中的认知辅助服务。该模型基于：1）利用多层感知器（MLP）神经网络模型和可能性逻辑的混合级融合；2）用于识别非直接可观察情绪的表达情感知识表示和推理模型；该模型联合利用了情感上层本体 (EmUO) 和 NKRL 语言支持的事件 HTemp 的 n 元本体。为了验证所提出的方法的目的，实验是使用 YouTube 数据集进行的，并在一个致力于智能设备陈列室中访问者认知帮助的真实场景中进行。结果表明，所提出的多模态情感识别模型优于所有基线模型。现实世界的场景证实了所提出的方法在情感上下文识别和管理方面以及在创建基于情感的辅助服务方面的有效性。实验是使用 YouTube 数据集进行的，并在一个致力于智能设备陈列室中访问者认知帮助的真实场景中进行。结果表明，所提出的多模态情感识别模型优于所有基线模型。现实世界的场景证实了所提出的方法在情感上下文识别和管理方面以及在创建基于情感的辅助服务方面的有效性。实验是使用 YouTube 数据集进行的，并在一个致力于智能设备陈列室中访问者认知帮助的真实场景中进行。结果表明，所提出的多模态情感识别模型优于所有基线模型。现实世界的场景证实了所提出的方法在情感上下文识别和管理方面以及在创建基于情感的辅助服务方面的有效性。