Piezoelectric Touch Sensing and Random-Forest-Based Technique for Emotion Recognition
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 st...
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| Published in | IEEE transactions on computational social systems Vol. 11; no. 5; pp. 6296 - 6307 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Piscataway
IEEE
01.10.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2329-924X 2373-7476 |
| DOI | 10.1109/TCSS.2024.3392569 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Jia, Weichen Tang, Chenyu Qi, Yuqing Gao, Shuo Dai, Yanning Zhou, Fuqiang Feng, Lulei |
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| SubjectTerms | 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 |
| Title | Piezoelectric Touch Sensing and Random-Forest-Based Technique for Emotion Recognition |
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