Mental Health First Responder AI-Buddy App : A Monitoring Service Framework for Early Detection and Continuous Management

Abstract

This thesis discusses a pressing issue in mental health care to address the growing need for continuous, personalized monitoring especially for neurodivergent individuals. The proposed solution is the AI-Buddy App, an AI-driven system that proposes to analyze diverse data streams to provide real-time insights about an individual’s mental status. The idea behind this concept is to fill a gap that has always existed between traditional assessments, which are periodic and subjective. The system proposes employing cutting-edge AI techniques, such as Long Short-Term Memory (LSTM) networks, to analyze trends over time. Natural Language Processing (NLP) detects emotional tone in text and voice inputs, and multimodal fusion methods combine these varied data sources into collective and cohesive insights. Predictive analytics, powered by Recurrent Neural Networks (RNNs), anticipate potential mental health declines, allowing for timely interventions. To keep users engaged, the app proposes to incorporate elements of gamification and behavioral prompts. This monitoring system also addresses significant concerns about data privacy, safety, regulatory compliance, and its integration into existing infrastructures. The research draws insights from case studies and data gathered through wearables and mobile apps. Although still a conceptual model, pending clinical testing, it considers key implementation challenges, including data privacy (with compliance to GDPR and NIS2 laws), algorithmic fairness, and scalability, where solutions like edge computing are explored. By emphasizing ethical AI development and adaptable, user-friendly design, this thesis lays the groundwork for a scalable, inclusive mental health tool. Future directions include real-world testing and hybrid care approaches that combine AI insights with human support.