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@article{195093,
        author = {Reddiboina Leela Krishna and GUDETI SUDARSHAN ARAVIND REDDY and Rudrapankthi Naveen},
        title = {Smart SoC-Based Mental Health Monitoring System Using AI and Multimodal Sensors},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {7923-7931},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195093},
        abstract = {Mental health disorders such as stress, anxiety, and depression are increasing globally, requiring continuous and real-time monitoring systems for early detection. This paper presents a Smart System-on-Chip (SoC)-based mental health monitoring system that integrates physiological, environmental, and behavioral data using multimodal sensors and artificial intelligence techniques. The system utilizes sensors such as heart rate (BPM), temperature, air quality, and accelerometer along with a camera module for facial emotion recognition. Sensor data is processed using an FPGA for efficient signal handling, while a Raspberry Pi performs AI-based analysis using Deep Face and TensorFlow models.
The proposed system combines physiological signals with real-time emotion detection to provide a comprehensive mental health assessment. Data is securely transmitted to a cloud platform (Azure IoT Hub) for storage, monitoring, and personalized recommendations. The system offers a low- cost, scalable, and real-time solution for early detection of mental health issues and supports remote healthcare applications. This work aligns with UN Sustainable Development Goal 3 (Good Health and Well-being) by enabling accessible and proactive mental healthcare.},
        keywords = {Mental Health Monitoring, FPGA, Raspberry Pi, Deep Face, OpenCV, Multimodal Sensors, AI, Emotion Detection, IoT, Azure Cloud},
        month = {March},
        }