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Documentation Index

Fetch the complete documentation index at: https://learn.pcbcupid.com/llms.txt

Use this file to discover all available pages before exploring further.

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Edge Impulse Gesture Recognition

Build a gesture detection system that runs fully offline on your ESP32. This guide uses Edge Impulse to train a model and export it as a ready-to-use Arduino library.

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How it Works? (The Simple Version)

  1. Data: You record motion data (gestures) using your smartphone or sensor.
  2. Train: Edge Impulse uses that data to teach an AI model to recognize those patterns.
  3. Deploy: You export the model as code and upload it to your ESP32. It then runs locally without needing the internet.

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What You Need

Before starting, make sure you have your hardware and software environment ready.

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Hardware Required

  • ESP32 DevKit (or any ESP32 board).
  • IMU Sensor (e.g., MPU6050 or LSM6DS3).
  • Smartphone (To record initial data).
  • USB Cable (For power and communication).
Circuit Diagram

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Software

  • Arduino IDE: To upload the code.
  • ESP32 Board Package: Installed via Arduino Boards Manager.
  • Edge Impulse Account: To train and export your model.

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Software Setup

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Step 1: Create a Project

Go to edgeimpulse.com, log in, and create a new project named something like “Gesture Detection”. Project creation

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Step 2: Connect Your Phone

In the Devices tab, connect your smartphone by scanning the QR code. Your phone now acts as the motion sensor for data collection. Connect your phone

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Step 3: Collect Data

Go to Data Acquisition, type a label like "up-down", hit Start Sampling, and perform the gesture. Repeat for each gesture you want to teach. Keep the data balanced and save some as Test Data. Collect data

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Step 4: Design the Impulse

Go to Impulse Design:
  1. Set Window Size to ~2 seconds.
  2. Add Spectral Analysis as the processing block.
  3. Add Neural Network as the learning block.
Design impulse

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Step 5: Check Accuracy

Open Feature Explorer and check that your gestures form separate clusters. The more separated they are, the better your accuracy will be. Check accuracy

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Step 6: Train the Model

Go to the Classifier tab and hit Start Training. Expect around 80–90% accuracy. Train the model
If accuracy is low, collect more data or be more consistent with your gestures.

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Step 7: Export as Code

Go to Deployment, select Arduino Library, and download the ZIP file. This is your AI model exported as standard C++ code. Export as code

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Step 8: Upload to ESP32

  1. Extract the ZIP file.
  2. Open Arduino IDE and go to File → Examples → [Your Project Name] → ESP32 → ESP32 Fusion.
  3. Install the ESP32 board package, wire up your ESP32 + IMU sensor, select the right board and COM port, then click Upload.
Upload to ESP32

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Step 9: Test Your Gestures

Open the Serial Monitor and perform your gestures. You will see real-time predictions like "Gesture: up-down". It runs fully offline on the ESP32! Test Your Gestures Have fun with your mini AI Assistant!