Piezoelectric Touch Sensing and Random-Forest-Based Technique for Emotion Recognition

• Published in: IEEE transactions on computational social systems Vol. 11; no. 5; pp. 6296 - 6307
• Main Authors: Qi, Yuqing, Jia, Weichen, Feng, Lulei, Dai, Yanning, Tang, Chenyu, Zhou, Fuqiang, Gao, Shuo
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Arousal; Cognition; Electrodes; Emotion recognition; Emotions; Feature extraction; Force; Games; Keystroke dynamics; Machine learning; Piezoelectric devices; piezoelectric touch panel; Piezoelectricity; Real time; Sensors; Smart phones; System reliability
• ISSN: 2329-924X; 2373-7476
• DOI: 10.1109/TCSS.2024.3392569

Emotion recognition, a process of automatic cognition of human emotions, has great potential to improve the degree of social intelligence. Among various recognition methods, emotion recognition based on touch event's temporal and force information receives global interests. Although previous studies have shown promise in the field of keystroke-based emotion recognition, they are limited by the need for long-term text input and the lack of high-precision force sensing technology, hindering their real-time performance and wider applicability. To address this issue, in this article, a piezoelectric-based keystroke dynamic technique is presented for quick emotion detection. The nature of piezoelectric materials enables high-resolution force detection. Meanwhile, the data collecting procedure is highly simplified because only the password entry is needed. International Affective Digitized Sounds (IADS) are applied to elicit users' emotions, and a pleasure-arousal-dominance (PAD) emotion scale is used to evaluate and label the degree of emotion induction. A random forest (RF)-based algorithm is used in order to reduce the training dataset and improve algorithm portability. Finally, an average recognition accuracy of 79.33% of four emotions (happiness, sadness, fear, and disgust) is experimentally achieved. The proposed technique improves the reliability and practicability of emotion recognition in realistic social systems.