Overview / Usage

The Real-time Student Mental Health Analysis project aims to harness the capabilities of Intel One API and OpenVINO toolkit for efficient and high-performance facial expression recognition. Using IoT devices equipped with cameras, the system continuously captures and analyzes students' facial expressions in real-time.

The core of the project relies on Intel One API's optimization features, ensuring seamless execution across various Intel hardware architectures. The OpenVINO toolkit is employed to deploy and run pre-trained deep learning models for facial expression recognition, enhancing inference speed and accuracy.

As students engage with educational content, the system monitors their facial expressions to detect emotional states such as happiness, sadness, stress, or frustration. Machine learning algorithms, optimized through Intel One API, process this data to provide real-time insights into the students' mental well-being.

The solution offers a user-friendly dashboard for educators and mental health professionals, providing actionable information to support timely interventions. Alerts can be generated for instances of potential distress, enabling educators to respond promptly and appropriately. The project prioritizes privacy and ethical considerations, ensuring that data is handled securely and anonymously.

The integration of Intel One API not only ensures efficient utilization of Intel hardware but also facilitates scalability, allowing the system to be deployed in educational institutions of varying sizes. Through this project, we aim to contribute to student well-being by leveraging cutting-edge technologies to provide valuable insights and support within an educational environment.

Methodology / Approach

The methodology for the Real-time Student Mental Health Analysis project revolves around leveraging Intel One API and the OpenVINO toolkit to address the challenge of assessing students' mental well-being through real-time facial expression analysis. We define the problem scope, select technologies optimized for Intel hardware, integrate IoT devices for facial expression capture, and deploy pre-trained deep learning models. Emphasis is placed on real-time data processing, machine learning algorithm optimization using Intel One API, and robust privacy measures. A user-friendly dashboard is developed for educators and mental health professionals, enabling real-time insights and distress alerts. The system is designed for scalability across educational institutions, with thorough testing, documentation, and training to ensure a technology-driven, ethical, and effective solution.

Technologies Used

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• Intel One API
• OpenVINO toolkit (Open Visual Inference and Neural network Optimization)
• Internet of Things (IoT)
• Machine Learning (ML)
• Computer Vision
• Real-time Image Processing