Depression Detection through Integrative Multimodal Signals: Exploring Advanced Computational Techniques
Year: 2024
Type: article
Abstract: This research paper presents a comprehensive exploration of advanced methodologies in affective computing aimed at enhancing the detection of depression, a condition impacting millions globally. We propose an innovative model that integrates machine learning algorithms with multimodal data analysis to facilitate real-time monitoring and early intervention. Our approach synthesizes data from facial expression analysis, speech pattern recognition, and physiological signal processing, creating a robust depression detection system. Through rigorous experimentation, we demonstrate that this integrated methodology significantly improves the accuracy and reliability of depression diagnosis compared to traditional methods. The findings underscore the potential of affective computing technologies to transform mental health monitoring and support, offering new avenues for timely identification and intervention strategies in clinical settings. This work not only contributes to the field of mental health but also paves the way for future research in automated emotional assessment and intervention systems.
Authors: Mohammed Mahdi Allarakhia, Mubashira Shaikh, Hussain Sidhpurwala, Ayesha Sayyed, Dr. Ashfaq Shaikh
Institution: M.H. Saboo Siddik College Of Engineering
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4403318755