Dyadic interactions are significant for human life. Most body sensor networks-based research studies focus on daily actions, but few works have been done to recognize affective actions during interactions. The purpose of this paper is to analyze and recognize affective actions collected from dyadic interactions.,A framework that combines hidden Markov models (HMMs) and k-nearest neighbor (kNN) using Fisher kernel learning is presented in this paper. Furthermore, different features are considered according to the interaction situations (positive situation and negative situation).,Three experiments are conducted in this paper. Experimental results demonstrate that the proposed Fisher kernel learning-based framework outperforms methods using Fisher kernel-based approach, using only HMMs and kNN.,The research may help to facilitate nonverbal communication. Moreover, it is important to equip social robots and animated agents with affective communication abilities.,The presented framework may gain strengths from both generative and discriminative models. Further, different features are considered based on the interaction situations.

中文翻译：

---

基于生成和判别模型的二元交互中的情感动作识别

二元相互作用对人类生活很重要。大多数基于身体传感器网络的研究都集中在日常行为上，但很少有工作来识别交互过程中的情感行为。本文的目的是分析和识别从二元交互中收集的情感动作。本文提出了一种使用 Fisher 核学习结合隐马尔可夫模型 (HMM) 和 k-最近邻 (kNN) 的框架。此外，根据交互情况（积极情况和消极情况）考虑不同的特征。本文进行了三个实验。实验结果表明，所提出的基于 Fisher 核学习的框架优于仅使用 HMM 和 kNN 的基于 Fisher 核的方法。该研究可能有助于促进非语言交流。此外，为社交机器人和动画代理配备情感交流能力也很重要。所提出的框架可以从生成模型和判别模型中获得优势。此外，基于交互情况考虑不同的特征。