Emotion recognition or detection is broadly utilized in patient–doctor interactions for diseases, such as schizophrenia and autism and the most typical techniques are speech detection and facial recognition. However, features extracted from these behavior-based emotion recognitions are not reliable since humans can disguise their emotions. Recording voices or tracking facial expressions for a long term is also not efficient. Therefore, our aim is to find a reliable and efficient emotion recognition scheme, which can be used for nonbehavior-based emotion recognition in real time. This can be solved by implementing a single-channel electrocardiogram (ECG)-based emotion recognition scheme in a lightweight embedded system. However, existing schemes have relatively low accuracy. For instance, the accuracy is about 82.78% by using a least squares support vector machine (SVM). Therefore, we propose a reliable and efficient emotion recognition scheme—exploitative and explorative gray wolf optimizer-based SVM (X-GWO-SVM) for ECG-based emotion recognition. Two data sets, one raw self-collected iRealcare data set, and the widely used benchmark WESAD data set are used in the X-GWO-SVM algorithm for emotion recognition. Leave-single-subject-out cross-validation yields a mean accuracy of 93.37% for the iRealcare data set and a mean accuracy of 95.93% for the WESAD data set. This work demonstrates that the X-GWO-SVM algorithm can be used for emotion recognition and the algorithm exhibits superior performance in reliability compared to the use of other supervised machine learning methods in earlier works. It can be implemented in a lightweight embedded system, which is much more efficient than existing solutions based on deep neural networks.

中文翻译：

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一种用于智能情绪识别的新型开发性和探索性 GWO-SVM 算法

情绪识别或检测广泛用于精神分裂症和自闭症等疾病的医患互动，最典型的技术是语音检测和面部识别。然而，从这些基于行为的情绪识别中提取的特征并不可靠，因为人类可以掩饰自己的情绪。长期录制语音或跟踪面部表情的效率也不高。因此，我们的目标是找到一种可靠且高效的情绪识别方案，可用于实时的非行为情绪识别。这可以通过在轻量级嵌入式系统中实施基于单通道心电图 (ECG) 的情绪识别方案来解决。然而，现有方案的准确性相对较低。例如，准确度约为 82。78% 通过使用最小二乘支持向量机 (SVM)。因此，我们提出了一种可靠且有效的情绪识别方案——用于基于 ECG 的情绪识别的基于灰狼优化器的开发性和探索性支持向量机 (X-GWO-SVM)。X-GWO-SVM 算法使用两个数据集，一个原始的自收集 iRealcare 数据集和广泛使用的基准 WESAD 数据集进行情绪识别。排除单个受试者交叉验证对 iRealcare 数据集产生的平均准确度为 93.37%，对 WESAD 数据集的平均准确度为 95.93%。这项工作表明，X-GWO-SVM 算法可用于情感识别，并且与早期工作中使用其他监督机器学习方法相比，该算法在可靠性方面表现出优越的性能。它可以在轻量级嵌入式系统中实现，