College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.
Zhejiang Key Laboratory for Pulsed Power Tanslational Medicine, Hangzhou Ruidi Biotech Ltd., Hangzhou 310000, China.
Sensors (Basel). 2021 Feb 2;21(3):1018. doi: 10.3390/s21031018.
Emotion recognition is of great importance for artificial intelligence, robots, and medicine etc. Although many techniques have been developed for emotion recognition, with certain successes, they rely heavily on complicated and expensive equipment. Skin potential (SP) has been recognized to be correlated with human emotions for a long time, but has been largely ignored due to the lack of systematic research. In this paper, we propose a single SP-signal-based method for emotion recognition. Firstly, we developed a portable wireless device to measure the SP signal between the middle finger and left wrist. Then, a video induction experiment was designed to stimulate four kinds of typical emotion (happiness, sadness, anger, fear) in 26 subjects. Based on the device and video induction, we obtained a dataset consisting of 397 emotion samples. We extracted 29 features from each of the emotion samples and used eight well-established algorithms to classify the four emotions based on these features. Experimental results show that the gradient-boosting decision tree (GBDT), logistic regression (LR) and random forest (RF) algorithms achieved the highest accuracy of 75%. The obtained accuracy is similar to, or even better than, that of other methods using multiple physiological signals. Our research demonstrates the feasibility of the SP signal's integration into existing physiological signals for emotion recognition.
情感识别对于人工智能、机器人和医学等领域都非常重要。尽管已经开发了许多情感识别技术，并取得了一定的成功，但这些技术都严重依赖于复杂和昂贵的设备。皮肤电位（SP）长期以来一直被认为与人类的情绪有关，但由于缺乏系统的研究，它在很大程度上被忽视了。在本文中，我们提出了一种基于单一 SP 信号的情感识别方法。首先，我们开发了一种便携式无线设备来测量中指和左手腕之间的 SP 信号。然后，我们设计了一个视频诱导实验，以刺激 26 名被试者的四种典型情绪（快乐、悲伤、愤怒、恐惧）。基于该设备和视频诱导，我们获得了一个由 397 个情感样本组成的数据集。我们从每个情感样本中提取了 29 个特征，并使用八种成熟的算法基于这些特征对四种情绪进行分类。实验结果表明，梯度提升决策树（GBDT）、逻辑回归（LR）和随机森林（RF）算法的准确率最高，达到了 75%。我们的研究结果表明，SP 信号与现有的生理信号相结合进行情感识别是可行的。
