PocketPsyc: A Multi-Model AI System for Real-Time Emotion-Aware Mental Health Support

Ayesha Maqsood1, Altaf Hussain1, Marran Al Qwaid2, Zara Haider1, Muazzam A. Khan Khattak3

  1. Department of Computer Science, KICSIT Campus, Institute of Space Technology, Islamabad
    44000, Pakistan
    altaf.hussain@ist.edu.pk (corresponding author)
  2. Department of Computer Science, College of Computing and Information Technology, Shaqra University, Saudi Arabia
    maldossari@su.edu.sa
  3. Computer Science Department, Quaid-i-Azam University, Islamabad, Pakistan
    muazzam.khattak@qau.edu.pk (corresponding author)

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-based 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.

Key words

Mental Health Support, Cognitive Behavioral Therapy, Emotion Recognition, Multi-Model AI Systems, Mobile Health

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS260524041M

Publication information

Volume 23, Issue 4 (September 2026)
Year of Publication: 2026
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Maqsood, A., Hussain, A., Al Qwaid, M., Haider, Z., Khattak, M.A.K.: PocketPsyc: A Multi-Model AI System for Real-Time Emotion-Aware Mental Health Support. Computer Science and Information Systems, 23(4) (2026). https://doi.org/10.2298/CSIS260524041M