ESP32 TFLite Motion Classifier Kit with ESP32 + MPU6050
Build a Motion Classifier with ESP32, MPU6050, and On-Device Machine Learning
Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.
This is not a beginner blinky project. You'll build a wearable device that captures real-time accelerometer and gyroscope data from the MPU6050, runs a custom TensorFlow Lite model directly on the ESP32, and classifies human motion — walking, running, or idle — lighting up the OLED instantly. It’s a full edge AI pipeline, from sensor to inference, condensed into one kit perfect for engineering final years, hackathon builds, and serious makers who want to enter embedded machine learning.
What You'll Build
By the end of this 5-6 hour build, you’ll have a working activity classifier on a breadboard or custom PCB. Strap it to your wrist or clip it to your belt, and the system will predict your movement state with sub-second latency. The OLED displays the current class and confidence score — proving that a microcontroller costing a few hundred rupees can perform real-time ML without a cloud roundtrip.
What You'll Learn
- Capture 6-axis IMU data (accel + gyro) and stream it over I2C for model training
- Preprocess raw sensor signals into feature windows ready for Edge Impulse
- Design, train, and export a TensorFlow Lite model for ESP32 using the Edge Impulse platform
- Deploy a neural network inference loop in Arduino IDE, managing memory and timing on a dual-core microcontroller
Kit Contents
| Component | Quantity |
|---|---|
| ESP32 Dev Board | 1 |
| MPU6050 | 1 |
| 0.96in OLED | 1 |
| 4.7kΩ Resistors | 5 |
| 100nF Capacitors | 5 |
| PCB Prototype Board | 2 |
| Micro USB Cable | 1 |
| Soldering Iron | 1 |
| Solder Wire | 1 |
Why Buy This Kit Instead of Sourcing Parts Separately
| Factor | Sourcing Separately | Compoden Kit |
|---|---|---|
| Compatibility checks | You verify each sensor, resistor value, and voltage logic | Pre-tested as a system — MPU6050 and OLED pull-ups, I2C addresses, firmware libraries are resolved |
| Build support | Forums and scattered tutorials | AI companion trained on this exact project — wiring, power, and model deployment steps |
| Time to first working build | Days of debugging bus errors and model load failures | Hours, with step-by-step guidance and pre-verified model header file |
| Shipping coordination | Multiple sellers, multiple delays | One shipment from Bengaluru in 3-5 days |
Who This Kit Is For
Advanced students and professional developers will get the most from this kit. It’s tailored for B.Tech ECE, EEE, and CSE final-year project work, Smart India Hackathon teams building wearable health or sports tech, and research scholars prototyping activity recognition at IITs, NITs, VIT, or BITS Pilani. If you’ve already completed beginner Arduino projects and have basic C++ and circuit wiring knowledge, you’re ready to take on edge AI.
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.
What if I get stuck during the build?
Scan the QR code inside your box to launch the AI companion. It knows the exact pinout, libraries, and model import steps. If you need human help, our WhatsApp line connects you to a real engineer who has built this kit.
Is soldering experience mandatory?
You’ll need to solder the MPU6050 and OLED headers onto the prototype board. The kit includes a soldering iron and lead-free solder. If you’re new to soldering, the AI companion includes a practice guide and clear photos of each joint.
Can I train my own motion model instead of the pre-trained one?
Yes. The AI companion walks you through Edge Impulse data collection, feature engineering, and model export. You can record your own walking, running, and idle data, retrain the TFLite model, and replace the header file to classify new activities like jumping or cycling.
Does this kit work for CBSE Class 12 or ATL projects?
This kit is designed for college-level and advanced hobbyists. The TinyML pipeline and I2C interfacing are beyond the typical Class 12 syllabus, but motivated ATL mentors and 12th-grade students with prior Arduino experience can complete it with the AI companion’s support.
MPU6050 feeds Edge Impulse TFLite model on ESP32. Classifies walking, running and idle states on OLED.
What's in this kit
Choose your assembly option:
- Soldering Kit — 25W soldering iron, 60/40 solder wire, flux, and small perfboard for permanent assembly.
- Breadboard Combo — 800-point full-size breadboard with 65-piece jumper wire pack for solderless prototyping.
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Shipping Information
- Prepaid Orders: ₹75 for orders up to ₹999, FREE shipping above ₹999
- COD Orders: ₹125 shipping + ₹50 COD fee = ₹175 total
- Delivery Timeline: Dispatch in 1-2 days, delivery in 2-7 days depending on location
Returns & Warranty
- 7-Day Return: Manufacturing defects only (approval required)
- Warranty: 7 days from delivery
- Non-Returnable: Batteries, consumables, cut wires, clearance items