Serencoach: an Ai-Driven Persuasive Digital Coach for Anxiety and Depression Management
Bibliographic record
Abstract
Despite the prevalence of mental health issues such as anxiety and depression, access to timely and personalized care remains limited. SerenCoach is an AI-driven, persuasive, and mobile-based digital coach designed to support anxiety and depression management through multimodal analysis of facial expressions and voice. By leveraging advanced technologies, including Facial Expression Recognition (FER) model, Large Language Models (LLMs) such as Llama3, and persuasive strategies, SerenCoach assesses users' anxiety and depression risk levels and provides personalized interventions. SerenCoach's methodology involves engaging in a conversation/dialogue with users while analyzing facial expressions and verbal expression of personal experiences or health condition (which is automatically converted to text) to assess their risk level. Based on the assessed risk - categorized as low, medium, or high - the app delivers personalized and evidence-based interventions including guided meditation, gratitude journaling, AI-powered therapist, and access to emergency services. SerenCoach motivates users through goal setting, reminder, and progress tracking to enhance user engagement. This paper discusses the design and development of SerenCoach, and demonstrates its ability to improve mental wellbeing through interactive, adaptive, and real-time support.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".