Emotion Recognition Based on Skin Potential Signals with a Portable Wireless Device

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 3; p. 1018
• Main Authors: Chen, Shuhao, Jiang, Ke, Hu, Haoji, Kuang, Haoze, Yang, Jianyi, Luo, Jikui, Chen, Xinhua, Li, Yubo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 02.02.2021; MDPI AG
• Subjects: Algorithms; Artificial Intelligence; emotion recognition; Emotions; gradient-boosting decision tree; Humans; Logistic Models; portable device; Skin; skin potential; Wireless Technology; gradient-boosting decision tree; skin potential; emotion recognition; portable device
• DOI: 10.3390/s21031018
• ISSN: 1424-8220

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.