GenAI-Powered Mental Health Companion Bot with Real-Time Burnout Detection and Crisis-Triage Protocol Using Multimodal Sentiment Analysis

Abstract

The text explains the basics of drone design, focusing on how quadrotor systems work and how they differ from traditional helicopters. Unlike conventional helicopters that rely on complex rotor systems and swashplates for control, quadrotors use four independently controlled rotors. By adjusting the speed of each rotor, they achieve stable flight, directional movement, and maneuverability without needing mechanically complex parts, making them simpler and more efficient. The literature survey highlights research related to drone design, structural analysis, material selection, and performance optimization. Studies show the importance of reducing drone weight, improving structural strength, and selecting appropriate materials like aluminum or carbon fiber. Some research also focuses on payload delivery systems, aerodynamic performance, and stress analysis using simulation tools like ANSYS. Additionally, real-world concerns such as pesticide exposure risks motivate the development of drones for safer applications. The methodology section explains drone payload optimization, which involves maximizing carrying efficiency while maintaining stability, battery life, and flight performance. Key factors include proper weight distribution, energy management, material selection, and software-based flight simulation. Engineering tools like CAD design and simulation software (e.g., CREO and ANSYS) are used to model, assemble, and analyze drone structures, including meshing and boundary condition setup.

Introduction

Mental health challenges like burnout, anxiety, and depression are rising globally, while access to timely and personalized care remains limited due to stigma, long wait times, and geographic barriers. Existing digital mental health tools help somewhat but are mostly reactive, responding only when users initiate interaction and lacking continuous monitoring.

The text proposes a more proactive AI-based mental health system that provides daily conversational check-ins through a warm, non-judgmental AI companion. Unlike traditional tools, it continuously tracks users’ emotional tone and language over time to build a personal baseline and detect gradual signs of burnout or decline. It also includes a real-time Crisis-Triage Protocol that identifies high-risk language, compiles recent mood history, and immediately alerts human counselors when urgent distress is detected.

A literature review shows that while prior research covers mental health chatbots, depression detection using NLP, burnout prediction, empathetic language models, and crisis detection systems, these approaches are fragmented. No existing system fully integrates continuous emotional tracking, conversational support, and real-time crisis escalation in one platform.

The proposed architecture uses a five-layer system built on AWS. It begins with data ingestion through app-based daily check-ins (text, voice, or mood sliders), processed via API Gateway and AWS Lambda. Data is stored in both DynamoDB (for real-time access) and S3 (for long-term storage), with AWS Glue handling periodic aggregation and dataset preparation. A central generative AI model manages both empathetic conversation and sentiment analysis, enabling ongoing emotional monitoring and support.

Conclusion

Mental health support systems have long suffered from the same gap that afflicts most reactive healthcare platforms: they respond to distress rather than anticipate it. This project closes that gap by combining a warm, GenAI-powered conversational companion with longitudinal sentiment tracking, automated burnout detection, and a real-time Crisis-Triage Protocol that routes urgent cases to human counselors before situations escalate beyond the reach of digital support. What we have built is a serverless, event-driven platform that is simultaneously a daily companion and an always-on monitoring system — one that treats each conversation not just as a support interaction but as a data point in a longer story about the user\'s mental health trajectory. It does not replace the therapist or counselor; it makes them dramatically more effective by ensuring they always have the context they need and are alerted the moment a user needs human contact. Future directions include: integration with wearable physiological data (heart rate variability, sleep metrics) to enrich the Burnout Risk Index beyond text-based signals; multilingual support to serve non-English-speaking users; a dedicated mobile application for friction-free daily check-ins; full HIPAA compliance certification to enable deployment in accredited clinical settings; and exploration of federated learning approaches that allow model improvement across user populations without centralizing sensitive conversation data.

References

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Copyright

Copyright © 2026 Arvind Raj P, Dhavanesh R, Muthukrishna M, Mr.K. Sivaganesan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.