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Saeful Amri
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Journal of Data Insights
Published by Universitas Muhammadiyah Semarang
ISSN : -     EISSN : 29882109     DOI : https://doi.org/10.26714/jodi
Core Subject : Science, Education,
Computer Science & IT Mathematics
The Journal of Data Insights is an open access publication for peer-reviewed scholarly journals. The Journal of Data Insights focuses on the processing, analysis and interpretation of data for data-driven decisions and solutions in industry, hospitals, government and universities. All articles should contain a validation of the proposed idea, e.g. through case studies, experiments, or a systematic comparison with other already practiced approaches. Two types of papers will be accepted: (1) a short paper discussing a single contribution to a particular new trend or idea, and; (2) a longer paper outlining a specific Research trends. As part of our commitment to scientific advancement, Journal of Data Insights follows an open access policy, which makes published articles freely available online without subscription.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 51 Documents
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Systematic Review of Multimodal Emotion Recognition in the Wild: Integrating Facial Expressions, Speech, and Physiological Signals for Enhanced Context-Aware Applications : Tinjauan Sistematis Pengenalan Emosi Multimodal di Lingkungan Alami: Mengintegrasikan Ekspresi Wajah, Ucapan, dan Sinyal Fisiologis untuk Aplikasi yang Lebih Sadar Konteks Muhammad Munsarif; Norshuhani Zamin; Richmond Ampah-Mensah
Journal of Data Insights Vol 4 No 1 (2026): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v4i1.1197

Abstract

Facial Emotion Recognition (FER) has become a critical component of affective computing, human–computer interaction, intelligent healthcare, adaptive education, and assistive technologies. This systematic literature review synthesizes recent developments in multimodal emotion recognition in the wild by examining how facial expressions, speech, physiological signals, deep learning architectures, and deployment technologies shape robust context-aware FER systems. Following the PRISMA protocol, literature was identified from Scopus using FER-related deep learning keywords, resulting in 202 initial records and 61 eligible studies for thematic synthesis, trend analysis, methodological classification, and qualitative interpretation. The findings show that FER research has shifted strongly from handcrafted features toward CNN-based deep learning, transfer learning, hybrid architectures, attention mechanisms, and bio-inspired optimization. Human–computer interaction emerged as the dominant research context, while healthcare, autism spectrum disorder screening, education, assistive technology, mining safety, and smart services represent increasingly important application domains. Transfer learning dominated robustness strategies, while multimodal fusion using facial images, speech, EEG, wearable sensors, and audio-visual signals gained stronger academic attention because it improves contextual understanding and reduces the limitations of unimodal FER. The synthesis also reveals persistent challenges, including poor generalization in uncontrolled environments, dataset imbalance, cultural variation, micro-expression recognition, computational complexity, real-time deployment, and limited explainability. The review contributes a multidimensional conceptual perspective that integrates multimodal sensing, deep learning optimization, edge/IoT deployment, and ethical-aware application design. Future research should prioritize lightweight multimodal FER, cross-cultural datasets, explainable AI, privacy-preserving learning, real-world clinical validation, and adaptive systems capable of operating reliably under noisy, dynamic, and socially sensitive conditions.

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