EMOCARE: XAI Enabled Emotion Detection and Health Guidance for Pregnancy and Postpartum Wellness

The present study explores a significant need in maternal healthcare by presenting EMOCARE, an explainable AI-based solution aimed at delivering personalized emotional and health assistance throughout pregnancy and the postpartum phase, which entails distinct wellness requirements. EMOCARE accurately identifies emotions via the analysis of physiological and behavioral data, providing clear, personalized health recommendations specifically designed for the emotional and mental well-being of expectant and new mothers. The system employs Long Short-Term Memory (LSTM) networks for temporal data analysis and SHAP (SHapley Additive exPlanations) for model interpretability, hence assuring precision and transparency in its predictions. This openness cultivates trust between healthcare practitioners and patients, empowering women with confidence in AI-generated health data and facilitating more proactive, educated wellness treatments. Future research intends to augment EMOCARE by integrating supplementary data sources and improving its explainability, allowing a more profound understanding of mental health dynamics throughout pregnancy and the postpartum period. Ultimately, EMOCARE aims to enhance maternal well-being by providing accurate, proactive AI-driven assistance, facilitating greater use of wearable health technologies in women's health management.