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@article{181477,
        author = {Mahesh Kadam and Mundhe Sudhanshu and Mr. Tanmay Dawange and Mohan Durge and Gaurav Nemade},
        title = {AI-Powered Behavioral Analytics for Predicting Mental Health Crises in Real Time},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {4073-4077},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181477},
        abstract = {Emotion-aware artificial intelligence (AI) has emerged as a groundbreaking development in the field of mental health monitoring, offering a promising approach to understanding and improving individuals' emotional well-being. These AI-driven systems leverage sophisticated affective computing techniques—such as facial recognition, voice tone analysis, and behavioral pattern recognition—to detect and track mood fluctuations in real time. Unlike traditional mental health interventions that often rely on self-reported data or passive observation, emotion-aware AI actively monitors users' emotional states through continuous, objective input from various data sources.
This dynamic and real-time monitoring is designed to detect emotional shifts as they occur, providing instant feedback and facilitating early intervention.
The central aim of this research is to explore the capabilities, practical
applications, and inherent challenges of emotion-aware AI systems, particularly in the realm of mental health. To achieve this, we conducted an in-depth review of the existing literature, supplemented by user surveys and expert interviews, to assess the effectiveness, accuracy, and ethical implications of these AI tools. The findings reveal that while emotion-aware AI holds substantial promise for improving mental health outcomes—by offering users valuable insights into their emotional states and enabling more proactive
management of psychological well-being—the impact of these technologies is heavily
contingent on factors such as system design, data transparency, and user trust.
The study emphasizes that, while the potential benefits are considerable, the success of emotion-aware AI in promoting mental health hinges on the integrity of its design and its ability to prioritize user privacy and security.
Ethical concerns surrounding data collection and usage, particularly when it comes to sensitive emotional data, are pivotal in shaping the future of this technology. The degree to which users feel confident in the system’s accuracy and trust that their personal data will be handled responsibly plays a crucial role in the broader adoption and acceptance of emotion-aware AI tools. Therefore, while emotion-aware AI offers new possibilities for mental health care, it must be developed and implemented with a careful balance of technological innovation and ethical responsibility.},
        keywords = {Emotion-Aware AI, Mental Health Monitoring, Affective Computing, Emotion Recognition, Digital Health Tools},
        month = {June},
        }