Kang et al., 2019
ViewHTML| Publication | Publication Date | Title |
|---|---|---|
| D’Mello et al. | The affective computing approach to affect measurement | |
| Larradet et al. | Toward emotion recognition from physiological signals in the wild: approaching the methodological issues in real-life data collection | |
| Nayak et al. | A Human–Computer Interaction framework for emotion recognition through time-series thermal video sequences | |
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| Ayata et al. | Emotion based music recommendation system using wearable physiological sensors | |
| US10261947B2 (en) | Determining a cause of inaccuracy in predicted affective response | |
| US9955902B2 (en) | Notifying a user about a cause of emotional imbalance | |
| Kaklauskas et al. | A review of AI cloud and edge sensors, methods, and applications for the recognition of emotional, affective and physiological states | |
| US10572679B2 (en) | Privacy-guided disclosure of crowd-based scores computed based on measurements of affective response | |
| US10198505B2 (en) | Personalized experience scores based on measurements of affective response | |
| US20170095192A1 (en) | Mental state analysis using web servers | |
| US20200342979A1 (en) | Distributed analysis for cognitive state metrics | |
| Pérez-Edgar et al. | Navigating through the experienced environment: Insights from mobile eye tracking | |
| US11430561B2 (en) | Remote computing analysis for cognitive state data metrics | |
| Kang et al. | A visual-physiology multimodal system for detecting outlier behavior of participants in a reality TV show | |
| Ganesh et al. | Deep learning techniques for automated detection of autism spectrum disorder based on thermal imaging | |
| US20190108191A1 (en) | Affective response-based recommendation of a repeated experience | |
| Giritlioğlu et al. | Multimodal analysis of personality traits on videos of self-presentation and induced behavior | |
| Abdulghafor et al. | An analysis of body language of patients using artificial intelligence | |
| Udahemuka et al. | Multimodal Emotion Recognition using visual, vocal and Physiological Signals: a review | |
| Skaramagkas et al. | eSEE-d: Emotional state estimation based on eye-tracking dataset | |
| Alghowinem et al. | Evaluating and validating emotion elicitation using English and Arabic movie clips on a Saudi sample | |
| Karbauskaitė et al. | Kriging predictor for facial emotion recognition using numerical proximities of human emotions | |
| Wang et al. | Implicit video emotion tagging from audiences’ facial expression | |
| Alharbi et al. | A survey of incorporating affective computing for human-system co-adaptation |