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Emotion Assessment Using Feature Fusion and Decision Fusion Classification Based on Physiological Data: Are We There Yet?

机译：使用基于生理数据的特征融合和决策融合分类的情感评估：我们是否存在？

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摘要

Emotion recognition based on physiological data classification has been a topic of increasingly growing interest for more than a decade. However, there is a lack of systematic analysis in literature regarding the selection of classifiers to use, sensor modalities, features and range of expected accuracy, just to name a few limitations. In this work, we evaluate emotion in terms of low/high arousal and valence classification through Supervised Learning (SL), Decision Fusion (DF) and Feature Fusion (FF) techniques using multimodal physiological data, namely, Electrocardiography (ECG), Electrodermal Activity (EDA), Respiration (RESP), or Blood Volume Pulse (BVP). The main contribution of our work is a systematic study across five public datasets commonly used in the Emotion Recognition (ER) state-of-the-art, namely: (1) Classification performance analysis of ER benchmarking datasets in the arousal/valence space; (2) Summarising the ranges of the classification accuracy reported across the existing literature; (3) Characterising the results for diverse classifiers, sensor modalities and feature set combinations for ER using accuracy and F1-score; (4) Exploration of an extended feature set for each modality; (5) Systematic analysis of multimodal classification in DF and FF approaches. The experimental results showed that FF is the most competitive technique in terms of classification accuracy and computational complexity. We obtain superior or comparable results to those reported in the state-of-the-art for the selected datasets.
机译：基于生理数据分类的情感识别一直是十多年越来越多的兴趣的主题。然而，在文献中缺乏系统分析，关于选择分类器，传感器方式，特征和预期精度范围，只是为了命名几个限制。在这项工作中，我们通过监督学习（SL），决策融合（DF）和特征融合（FF）技术使用多模式生理数据，即心电图（ECG），电寄存器活动，评估情感（EDA），呼吸（RES）或血容量脉冲（BVP）。我们工作的主要贡献是跨在情感识别（ER）最先进的五个公共数据集的系统研究，即：（1）令人争论空间中的ER基准数据集的分类性能分析; （2）总结了现有文献中报告的分类准确性的范围; （3）使用精度和F1分数，表征各种分类器，传感器方式，传感器模式和特征组合的结果; （4）探索为每种方式设置的扩展功能; （5）DF和FF方法中多式化分类的系统分析。实验结果表明，在分类准确性和计算复杂性方面是FF是最具竞争力的技术。我们以所选数据集的最先进的人报告的那些，我们获得优越或比较的结果。

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