Integrated Sensor Systems for Multi-modal Emotion Recognition
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".
Deadline for manuscript submissions: 31 May 2025 | Viewed by 2087
Special Issue Editor
Special Issue Information
Dear Colleagues,
Emotion recognition stands at the nexus of human–computer interaction, imbuing intelligent systems with the capacity to comprehend and respond to human affective states. In recent years, the paradigm of multi-modal emotion recognition has emerged as a focal point of research, harnessing the synergistic integration of diverse sensory modalities to achieve enhanced accuracy and robustness in discerning emotional cues.
This Special Issue seeks to explore the rich tapestry of challenges and opportunities inherent in multi-modal emotion recognition. By capitalizing on the sensor fusion of visual, auditory, physiological, and textual cues, researchers aim to unravel the intricate nuances of human emotions, paving the way for more empathetic and responsive artificial intelligence systems.
The proliferation of computer vision and machine learning technologies has underpinned remarkable advancements in multi-modal emotion recognition. Through the judicious application of convolutional neural networks, recurrent neural networks, and attention mechanisms, researchers have endeavored to distill meaningful insights from multimodal data streams, enabling machines to perceive, interpret, and respond to human emotions with unprecedented fidelity.
Key themes to be explored in this Special Issue include:
- Multi-Modal Fusion Techniques: Investigating methodologies for integrating information from disparate sensory modalities to enhance the robustness and discriminative power of emotion recognition systems.
- Cross-Modal Transfer Learning: Exploring strategies for leveraging knowledge across modalities to mitigate data sparsity and domain shift, facilitating more generalized and adaptive emotion recognition models.
- Contextual and Temporal Dynamics: Examining the influence of contextual factors and temporal dynamics on the perception and interpretation of emotions, and devising models capable of capturing the evolving nature of affective states over time.
- Ethical and Social Implications: Delving into the ethical considerations surrounding the deployment of multi-modal emotion recognition systems, and addressing concerns related to privacy, bias, and algorithmic fairness.
Researchers are invited to contribute original research articles, review papers, and case studies that shed light on the theoretical underpinnings, methodological innovations, and practical applications of multi-modal emotion recognition. By fostering interdisciplinary dialogue and collaboration, this Special Issue endeavors to propel the frontier of emotion-aware computing, unlocking new frontiers in human-centered artificial intelligence.
Through the synthesis of diverse perspectives and methodologies, we aim to chart a course towards more empathetic and emotionally intelligent machines, capable of forging deeper and more meaningful connections with their human counterparts. Join us in this collective endeavor to unravel the mysteries of human emotion and usher in a new era of emotionally aware computing.
Dr. Kamlesh Mistry
Guest Editor
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Keywords
- multi-modal fusion
- cross-modal transfer learning
- contextual and temporal dynamics
- ethical and social implications
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