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@article{175982,
        author = {Keerthana R and Hemal Babu H and Naveen B and Lokeshwaran S},
        title = {AI–Powered Emotion Tracking System for Workplace Well-Being},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {4747-4753},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175982},
        abstract = {Employee well-being is a crucial factor in workplace productivity, job satisfaction, and overall organizational success. This research introduces an AI-powered facial expression recognition system designed to detect stress and depression among employees in real time. The system utilizes the Tiny Face Detector for efficient face detection, leveraging fiducial landmark detection powered by deep Convolutional Neural Networks (CNNs) to enhance accuracy. A CNN-based Face Expression Recognition Model classifies emotions, including happiness, sadness, neutrality, and anger, enabling comprehensive emotional analysis. Additionally, a ResNet-based deep CNN model is integrated for age and gender detection, providing further insights into emotional patterns across different demographic groups. To facilitate real-time data management, the system employs a Django-based backend with PostgreSQL, where employee emotion data is periodically recorded and analyzed. The collected data is used to compute an individual’s average daily emotional state, enabling continuous monitoring and trend analysis. If prolonged negative emotional states are detected, an AI-powered chatbot provides personalized motivational messages or mental health recommendations. Furthermore, an interactive HR dashboard displays real-time emotional trends, enabling management to take proactive measures, such as offering mental health support. This AI-driven system highlights the transformative potential of emotion recognition technology in fostering a supportive work culture and improving employee mental health and productivity.},
        keywords = {Facial Expression Recognition, Emotion Detection, Deep Learning, CNN, Stress Detection, AI Chatbot, Workplace Well-being, Mental Health Monitoring, Django, PostgreSQL.},
        month = {April},
        }