Year: 2024
Type: conference-paper
Abstract: Daily emotion recognition (DER) faces difficulties due to different environments, especially outdoor settings. We envision that DER will enhance various applications. For example, a smart healthcare system could detect emotions in both indoor and outdoor settings. This study proposed a real-time monitoring method named envER, which integrates environmental data with physiological data from wearable devices. A comprehensive validation of envER was conducted, applying three feature selection methods, and two model validation techniques to the publicly available DAPPER dataset. All assessments highlight the importance of environmental data in emotion recognition, and when combining environmental data with body data consistently yields improved performance in both binary classifications of arousal and valence. Finally, envER can achieve the highest recognition accuracies of 87.2% for arousal and 95.5% for valence.
Authors: Jiqing Qu, Qi Li, Xuefeng Li, Hui Xiao
Institution: Tongji University
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Awards: G2953919658, G4548586172, G4606321376
OpenAlex ID: W4409181452