PocketPsyc: A multi-model AI system for real-time emotion-aware mental health support

Abstract

Mental health disorders affect over one billion people worldwide, with treatment gaps particularly severe in resource-constrained regions such as Pakistan, where only 0.19 psychiatrists are available per 100,000 population. This study presents PocketPsyc, a mobile-based system designed to deliver scalable, evidence based mental health support through Cognitive Behavioral Therapy (CBT). The system integrates three specialized AI models: a fine-tuned BART model for therapeutic response generation (88.2% BLEU score), a RoBERTa-base classifier for real-time emotion recognition (F1-score of 0.89 across seven categories), and a TinyLlama-1.1B model for personalized mindfulness guidance (rated 4.3/5 in human evaluations). To ensure user privacy, the platform employs client-side AES-256 encryption and row-level security mechanisms. Additionally, a hybrid crisis detection module combines clinical threshold monitoring with linguistic cue analysis to identify high-risk scenarios. The system achieves an average end-to-end response latency of 3.2 seconds. These findings demonstrate that PocketPsyc provides a technically feasible, privacy-preserving, and scalable solution for delivering AI-assisted mental health support in underserved populations.