Build a Real-Time Motion Classifier on ESP32-S3 with Edge Impulse and MPU6050

Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.

With this kit, you'll train a machine learning model on your PC using Edge Impulse, deploy it to the ESP32-S3, and strap the MPU6050 to your ankle to see real-time activity classification on the OLED. It's a complete end-to-end embedded ML project that mirrors industrial human activity recognition systems, scaled for learning.

What You'll Build

You'll wire the MPU6050 accelerometer to the ESP32-S3, collect motion data for three activities, train a neural network in Edge Impulse, and deploy the TFLite model to classify walking, running, and idle states. The OLED displays predicted activity and confidence scores instantly, turning raw sensor data into actionable insight.

What You'll Learn

• Collecting and labeling accelerometer data for machine learning
• Designing and training a neural network using Edge Impulse's web interface
• Deploying a TensorFlow Lite model on the ESP32-S3 for real-time inference
• Displaying sensor outputs and ML predictions on an OLED screen

Kit Contents

Why Buy This Kit Instead of Sourcing Parts Separately

Who This Kit Is For

This kit is ideal for CBSE Class 11-12 students exploring AI and IoT for their practical exams, B.Tech ECE/EEE undergraduates building mini-projects, and teams preparing for Smart India Hackathon or ATL Tinkering Lab innovation challenges. If you've tinkered with Arduino and want to step into embedded machine learning, this kit provides the perfect guided pathway.

Built and Backed by Compoden

Every Compoden kit ships with an AI build companion trained on this exact project — accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.