INFRASENTINAL MONITORING SYSTEM

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  Graphical representation with webcam real time monitoring

Inspiration

Our inspiration comes from addressing a real-world problem, enhancing student engagement and productivity. By visualizing behavior patterns, we empower educators to make informed decisions.

What it does

This software continuously analyzes student behaviors such as sleeping, yawning, phone usage, attentiveness and so on during class, overlays real-time charts on the video feed.

How we built it

We trained a machine learning model using Teachable Machine on a self-created dataset, deployed it for real-time inference, and integrated it with a visualization library to display student's activities while concurrently plotting a separate graph showing activity trends over time.

Challenges we ran into

Gathering diverse and accurately annotated data for training the model posed challenges and faced many dependency errors. Achieving efficient real-time processing of video feeds while maintaining accuracy presented technical hurdles in optimizing software for better responsiveness.

Accomplishments that we're proud of

Developing a machine learning model capable of accurately classifying student's activities in real-time represents a significant accomplishment.

What we learned

Optimizing machine learning models for real-time performance requires careful consideration of both computational efficiency and accuracy.

What's next for INFRASENTINAL MONITORING SYSTEM

We find this also as a product with huge potential and scalability. We can improve the accuracy of the product by improving the model and the data. We can implement this product in industries, workplaces, medical fields etc.

Built With

• matplotlib
• opencv
• python
• tenserflow
• tkinter