Work

Multi-Modal Emotion Detection and Tracking System Using AI Techniques

Year: 2025

Type: article

Abstract: Emotion detection significantly impacts healthcare by enabling personalized patient care and improving treatment outcomes. Single-modality emotion recognition often lacks reliability due to the complexity and subjectivity of human emotions. This study proposes a multi-modal emotion detection platform integrating visual, audio, and heart rate data using AI techniques, including convolutional neural networks and support vector machines. The system outperformed single-modality approaches, demonstrating enhanced accuracy and robustness. This improvement underscores the value of multi-modal AI in emotion detection, offering potential benefits across healthcare, education, and human–computer interaction.

Source: Computers

DOI: 10.3390/computers14100441

Authors: Werner Mostert, Anish Kurien, Karim Djouani

Institutions: Tshwane University of Technology, Université Paris-Est Créteil

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,172