A COMPREHENSIVE REVIEW OF MULTIMODAL ARTIFICIAL INTELLIGENCE SYSTEMS FOR MENTAL HEALTH ANALYSIS USING IMAGE, SPEECH, AND IOT-BASED PHYSIOLOGICAL DATA

Abstract

With the rising rates of depression, anxiety, stress, and other mental ailments, mental health disorders are now a significant global health concern. While mental health monitoring is ongoing, usual approaches to counselling and diagnosis have been found to have some drawbacks, such as subjective diagnosis, high cost, scarcity of mental health workers and inaccessibility in remote areas. With the new developments in Artificial Intelligence (AI) technology, intelligent and automated healthcare production solutions have been developed which will enhance mental health assessment and monitoring. A multimodal AI-based system based on image processing, speech processing and physiological sensor monitoring for mental health analysis is examined in this review paper. In this proposed multimodal system, facial expression analysis, speech emotion recognition and biomedical sensors like heart rate, respiratory rate, blood pressure, and skin temperature are all combined to provide more accurate assessment of psychological conditions. Behavioral and emotional patterns that are linked to mental disorders are identified using machine learning and deep learning algorithms. Moreover, the integration of the Internet of Things (IoT) enabled wearable devices improves system functionality by enabling real-time health monitoring and facilitating remote counselling application. This paper underscores the need for using multimodal data fusion over the traditional single-modality systems to increase the accuracy of predictions and decrease the number of false diagnoses, respectively. The review also emphasizes the role of AI in remote healthcare systems for delivering scalable, cost-effective, and continuous mental health assessment capabilities in today's healthcare landscape.