ReflectAI: An AI-Driven Multimodal Journaling App for Promoting Mental Well-being

Serenify: An AI-Driven Multimodal Digital Coach for Continuous Anxiety and Depression Management

Anxiety and depression are among the most prevalent mental health conditions worldwide, yet current clinical assessment tools rely on retrospective self-report, which is subject to recall bias and limited by infrequent clinical contact. Digital health technologies, including mHealth applications and AI-based emotion-detection systems, have emerged to support continuous monitoring, but most rely on manual mood logging or single-modality sensing, capturing only a partial view of the user’s emotional state. To address these limitations, we present Serenify, an AI-driven, multimodal, persuasive mobile application that integrates five passive sensing modalities: facial expression recognition, speech emotion detection, ambient audio classification, text-based emotion inference, and keystroke dynamics analysis. A fusion module aggregates the outputs of dedicated machine learning models into a composite emotional risk level that drives the delivery of personalized wellness interventions. Eight persuasive strategies drawn from the Persuasive Systems Design framework and Goal-Setting Theory are embedded across the application to promote sustained engagement. We evaluated Serenify through an expert evaluation with five Human-Computer Interaction (HCI) experts, using 10 heuristics adapted for persuasive health technologies. The results show an overall mean severity of 0.26 (SD = 0.23), with no rating exceeding cosmetic level, indicating little or no usability problems across the system. Finally, we reflect on our findings to offer three design recommendations for building multimodal persuasive mental health technologies.

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

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.

RELieF: A MERN Stack Based AI-Powered Mental Health Monitoring and Emotional Support System

The rapid growth of academic and workplace stress has significantly increased mental health concerns among students and professionals. Traditional therapeutic models lack real-time monitoring, personalization, and scalable accessibility. Existing digital platforms often provide static recommendations without integrating structured mood analytics, secure data management, and intelligent conversational assistance. This paper presents RELieF, a MERN stack-based AI powered mental health monitoring and emotional support system. The proposed framework integrates daily mood tracking, graphical analytics, conversational AI assistance, and gamification mechanisms within a secure and scalable web architecture. The frontend is developed using React.js, while the backend employs Node.js and Express.js with MongoDB Atlas for cloud-based data storage. JWT-based authentication ensures secure access and data privacy. Experimental evaluation conducted on structured test cases demonstrates consistent performance with an overall accuracy of 93.33%, high engagement consistency, and effective visualization of emotional patterns. The proposed system provides a modular, scalable, and user-centric approach toward digital mental wellness. Keywords: MERN Stack; Mental Health Monitoring; AI Chatbot; Mood Analytics; Web Application.

MindGuard: An AI-Powered Agentic Student Mental Wellness Monitoring and Intervention System

AI-Driven Integrated Caregiving and Health Monitoring Framework for Elderly Well-Being

The rapid growth of the aging population has brought about some serious challenges, particularly in managing illnesses, feelings of loneliness, cognitive decline, and mental health issues. Traditional caregiving methods often depend on occasional assessments and hands-on supervision, which can fall short in providing the ongoing and adaptable support that’s really needed. This paper introduces an innovative caregiving and monitoring framework powered by AI, aimed at offering integrated, real-time, and comprehensive assistance for older adults. The system harnesses the power of Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and health data analytics to combine physical health monitoring, nutrition planning, smart routine coaching, and therapist-led mental health support all in one platform. With features like voice-based conversations and journaling, it makes emotional expression and behavioral analysis more accessible, helping to gain a deeper insight into users’ mental well-being. Predictive analytics and anomaly detection are used to spot early signs of health risks and shifts in behavior, allowing for timely interventions. Plus, remote access means caregivers and healthcare professionals can keep an eye on users and offer informed advice. By shifting caregiving from a reactive approach to a proactive and preventive one, this system not only improves quality of life but also encourages independent living and eases the burden on caregivers.

Model Development and Feasibility of Real-World Deployment of Multimodal Input-Based Subtyping of Depression in Tele-Counseling for Scalable Mental Health Assessment.

The rapid growth of tele-counseling and the use of lay counselors in high-volume, low-resource mental health services has created a need for scalable tools for early detection and triage. Effective personalization now requires stratifying individuals by dominant symptom profiles, such as appetite, agency, anxiety, and sleep disturbances. Depression symptoms vary widely, even among those with similar scores, reflecting distinct psychophysiological and cognitive-affective patterns. In tele-mental-health settings, where contextual cues are limited, multimodal behavioral signals from natural interactions can complement traditional assessments. Using synchronized audio, video, and text data from the EDAIC dataset (N=275), we propose a multimodal learning framework to classify five clinically validated outcomes: Depression, Appetite disturbance, Agency impairment, Anxiety, and Sleep problems. We developed a comprehensive multimodal machine-learning pipeline, incorporating automated dataset construction, modality-specific feature extraction (acoustic, facial action unit, linguistic), and supervised learning with cross-validation. Labels were derived from validated scoring rules to ensure clinical relevance. Sentiment analysis revealed lower sentiment scores in participants with high Depression, Anxiety, or Agency scores, but no significant differences in Appetite or Sleep severity. Model performance was assessed across three scenarios: text (transcripts), phone calls (audio + transcript), and video calls (audio + video + transcript). Temporal models (CNN+BiLSTM) achieved over 65% accuracy across modalities, while a fine-tuned temporal model for depression detection using video calls reached an accuracy of 81% with an f1-score of 0.79, demonstrating that our approach performs on par with state-of-the-art methods while maintaining a substantially lower computational footprint suitable for edge deployment. SHAPley analysis identified key audio and video features for detecting Depression and other symptoms. Beyond depression screening alone, our framework is among the first to simultaneously stratify individuals across five clinically relevant symptom domains as per Research Domain Criteria (R-DOC) enabling more personalized and targeted intervention. A translational avatar-based interface validated system operability, demonstrating the potential for scalable, objective mental-health assessment in tele-counseling.