受控情绪刺激下基于机器学习算法的生理信号情绪识别
Emotion Recognition from Physiological Signals Using Machine Learning Algorithms Under Controlled Emotional Stimuli
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中文总结 AI 辅助
该研究在受控情绪刺激下，以ECG和GSR为数据，提取时域与频域特征，用XGBoost、Random Forest等树基模型开展情绪分类，发现ECG+GSR特征集组合性能最优，为情绪识别系统提供了支撑。
中文摘要 AI 辅助
利用生理信号进行情绪识别在福祉分析、情感计算和人机交互中发挥着关键作用。本研究探究了多种机器学习模型在对不同粒度的离散情绪、效价和唤醒度等目标进行分类时的性能，所用生理信号包括心电图（ECG）和皮肤电反应（GSR）。我们从ECG和GSR数据中提取了多种时域和频域特征，用于训练机器学习模型。结果表明，使用树基模型对较少类别的离散情绪进行分类，以及对唤醒度进行分类，均可获得良好的分类准确率；XGBoost对离散情绪分类的准确率为52.8%，Random Forest对唤醒度分类的准确率为53.6%。在这两种情况下，ECG+GSR特征集的组合均实现了最佳性能。这些发现凸显了生理信号在捕捉情绪状态方面的有效性，为其在情绪识别系统中的应用提供了支持。
英文摘要
Emotion recognition using physiological signals plays a crucial role in well-being analysis, affective computing and human-computer interaction. This study investigates the performance of multiple machine learning models in classifying targets such as discrete emotions with varying granularity, valence and arousal using physiological signals such as Electrocardiogram (ECG) and Galvanic Skin Response (GSR). In here, we extracted various time- and frequency-domain features from the ECG and GSR data to train machine learning models. The results indicate that categorizing discrete emotions with fewer emotions and categorizing arousal achieves good classification accuracy with tree-based models. XGBoost achieved accuracy of 52.8 % for classifying discrete emotions and Random Forest achieved accuracy of 53.6% for classifying arousal. In both the cases, the combination of ECG+GSR feature sets achieved best performance. These findings highlight the effectiveness of physiological signals in capturing emotional states and support their use for emotion recognition systems.
