Dimensional affect recognition is a challenging topic and current techniques do not yet provide the accuracy necessary for HCI applications. In this work we propose two new methods. The first is a novel self-organizing model that learns from similarity between features and affects. This method produces a graphical representation of the multidimensional data which may assist the expert analysis. The second method uses extreme learning machines, an emerging artificial neural network model. Aiming for minimum intrusiveness, we use only the heart rate variability, which can be recorded using a small set of sensors. The methods were validated with two datasets. The first is composed of 16 sessions with different participants and was used to evaluate the models in a classification task. The second one was the publicly available Remote Collaborative and Affective Interaction (RECOLA) dataset, which was used for dimensional affect estimation. The performance evaluation used the kappa score, unweighted average recall and the concordance correlation coefficient. The concordance coefficient on the RECOLA test partition was 0.421 in arousal and 0.321 in valence. Results show that our models outperform state-of-the-art models on the same data and provides new ways to analyze affective states.

中文翻译：

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来自 HRV 的维度影响识别：一种基于监督 SOM 和 ELM 的方法

维度影响识别是一个具有挑战性的话题，当前的技术还不能提供 HCI 应用所需的准确性。在这项工作中，我们提出了两种新方法。第一个是一种新颖的自组织模型，它从特征和影响之间的相似性中学习。该方法产生可以帮助专家分析的多维数据的图形表示。第二种方法使用极限学习机，一种新兴的人工神经网络模型。为了尽量减少干扰，我们只使用心率变异性，可以使用一小组传感器记录。这些方法用两个数据集进行了验证。第一个由不同参与者的 16 个会话组成，用于评估分类任务中的模型。第二个是公开可用的远程协作和情感交互 (RECOLA) 数据集，用于维度影响估计。绩效评估使用 kappa 分数、未加权平均召回率和一致性相关系数。RECOLA 测试分区的一致性系数在唤醒方面为 0.421，在效价方面为 0.321。结果表明，我们的模型在相同数据上的表现优于最先进的模型，并提供了分析情感状态的新方法。