MentalSense AI: Real-Time Mental Stress Detection using Multimodal Behavioral Analysis and Personalized Support

Mental stress can affect concentration, communication, decision-making, and well-being. This paper presents MentalSense AI, an integrated application for real-time, stress-related behavioral assessment and personalized wellness support. The system combines webcam-based facial-expression analysis, microphone-based voice analysis, confidence-weighted multimodal fusion, continuous monitoring, assessment history, and guided wellness support. The facial pathway uses MediaPipe-based face localization and a convolutional neural network trained on FER2013-style expression classes. The existing Voice V9 pipeline processes 3.5-second audio windows, extracts Mel-frequency cepstral coefficients (MFCCs), delta-MFCCs, spectral descriptors, and chroma features, applies ANOVA F-test feature selection, and uses Logistic Regression, Linear Support Vector Machine, and Extra Trees classifiers. The application uses a React frontend, Python/FastAPI services, and SQLite persistence. Interface screenshots illustrate the implemented monitoring and assessment workflow. Because the available project materials do not establish a reproducible, end-to-end benchmark, no overall accuracy or latency value is claimed. The paper identifies the evaluation needed to validate classification, fusion, and real-time behavior.