Piezoelectric Touch Sensing and Random-Forest-Based Technique for Emotion Recognition
Bibliographic Data
| ID | 22107802 |
|---|---|
| Authors | Yuqing Qi (0000-0002-5183-3358, Beihang University), Weichen Jia (0000-0002-0749-2859, Tsinghua University), Lulei Feng (0009-0001-7860-9882, Peking University Shenzhen Hospital), Yanning Dai (0000-0002-0463-1921, Beihang University), Chenyu Tang (0000-0002-6368-5639, University of Cambridge), Fuqiang Zhou (0000-0001-9341-9342, Beihang University), Shuo Gao (0000-0003-2239-5982, Beihang University) |
| Year | 2024 |
| Volume | 11 |
| Issue | 5 |
| Pages | 6296-6307 |
| Publication date | 2024-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2024.3392569 |
| OpenAlex | W4398788567 |
| Language | EN |
| References cited | 75 |
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
Emotion recognition · Human–computer interaction · Random forest · Remote sensing · Speech recognition · Color perception and design · Computer Science · Emotion and Mood Recognition · IoT-based Smart Home Systems · Artificial Intelligence · Geology
The Experience of Emotion
Assessing the effectiveness of a large database of emotion-eliciting films
Pleasure-arousal-dominance
What is Meant by Calling Emotions Basic
The World of Emotions is not Two-Dimensional
Core affect and the psychological construction of emotion.
RobinNet
Multi-Source Domain Transfer Discriminative Dictionary Learning Modeling for Electroencephalogram-Based Emotion Recognition
CogEmoNet
An Emotion Recognition Method Based on Eye Movement and Audiovisual Features in Mooc Learning Environment
Rethinking Auditory Affective Descriptors Through Zero-Shot Emotion Recognition in Speech
A Visual–Audio-Based Emotion Recognition System Integrating Dimensional Analysis
Evidence for a three-factor theory of emotions
A circumplex model of affect
An argument for basic emotions
| Citation velocity | historical |
|---|---|
| Highly cited | No |