Mobile-Based Hybrid Experience Sampling for Real-Time Emotion Detection and Mental Health Insights
Electrica, cilt.26, 2026 (ESCI, Scopus, TRDizin)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 26
- Basım Tarihi: 2026
- Doi Numarası: 10.5152/electrica.2026.25291
- Dergi Adı: Electrica
- Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, TR DİZİN (ULAKBİM)
- Anahtar Kelimeler: Deep learning model, emotional responses, experience sampling, health studies, mental health research
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- İstanbul Kültür Üniversitesi Adresli: Evet
Özet
The present study uses mobile technology to improve data dependability and application in mental health research by investigating the deployment of the Experience Sampling Method (ESM) for real-time analysis of emotional reactions. Designed using a cross-platform mobile app that combines audio, visual, and self-report survey data gathered via both random and time-based random sampling techniques. Over a 14-day period, participants received eight daily messages that prompted replies to structured questions gauging instantaneous emotional states. This hybrid data collection and processing system allows continuous monitoring of emotional dynamics, therefore offering high temporal resolution insights into users' psychological states. A convolutional neural network (CNN) architecture was employed to process data for emotion classification, achieving a 75% accuracy rate. With implications for individualized mHealth applications aiming at psychological health, the results show the potential of ESM combined with deep learning models in enhancing real-time emotional monitoring.