Deploy TensorFlow Lite Micro for Gesture Recognition on ESP32 with I2S Microphone

Introduction

In this tutorial, we will walk through the process of deploying a TensorFlow Lite Micro model for gesture recognition on the ESP32 using the I2S microphone input. This guide will help you set up your development environment, prepare your model, and deploy it on the ESP32 for real-time gesture recognition.

Prerequisites

• Basic knowledge of embedded systems and programming
• ESP32 development board
• Microphone compatible with I2S (such as INMP441)
• Arduino IDE installed
• TensorFlow Lite Micro library
• Python installed for model conversion

Parts/Tools

• ESP32 Development Board
• I2S Microphone (e.g., INMP441)
• USB cable for programming the ESP32
• Arduino IDE with ESP32 board support
• TensorFlow Lite Micro library
• Python and TensorFlow installed on your PC

Steps

Step 1: Set up your Arduino IDE

1. Open the Arduino IDE.
2. Go to File > Preferences.
3. In the “Additional Boards Manager URLs” field, add the ESP32 URL:

https://dl.espressif.com/dl/package_esp32_index.json

4. Go to Tools > Board > Boards Manager, search for “ESP32”, and install it.

Step 2: Connect the I2S Microphone

Connect the I2S microphone to the ESP32 as follows:

• INMP441 VCC to ESP32 3.3V
• INMP441 GND to ESP32 GND
• INMP441 SCK to ESP32 GPIO 14
• INMP441 WS to ESP32 GPIO 15
• INMP441 SD to ESP32 GPIO 32

Step 3: Prepare the TensorFlow Lite Micro model

1. Train your model using TensorFlow and save it in TensorFlow Lite format.
2. Convert the model to TensorFlow Lite Micro format using the following command:

python -m tensorflow_model_maker --model_dir=my_model --output_dir=my_model.tflite

3. Optimize your model for size and performance.

Step 4: Create the Arduino sketch

1. Create a new sketch in the Arduino IDE.
2. Include the necessary libraries at the top of your sketch:

#include <I2S.h>
#include <TensorFlowLite.h>

3. Define the I2S settings and initialize the microphone:

void setup() {
        I2S.begin(I2S_PHILIPS_MODE, I2S_BITS_PER_SAMPLE_32BIT, I2S_CHANNEL_FMT_ONLY_LEFT);
        // Additional initialization code
    }

Step 5: Load the model and run inference

1. Load your TensorFlow Lite Micro model in the setup function:

tflite::Model* model = tflite::GetModel(your_model_data);

2. In the loop function, read data from the microphone and run inference:

void loop() {
        // Read audio data from I2S
        // Run inference
        // Process results
    }

Troubleshooting

• Model not loading: Ensure the model data is correctly included in your sketch and that the path is correct.
• Microphone not responding: Check connections and ensure the I2S microphone is powered and correctly wired.
• Audio quality issues: Experiment with different sample rates or bit depths in your I2S configuration.
• Compilation errors: Ensure that all required libraries are installed, and the correct board is selected in the Arduino IDE.

Conclusion

Deploying a TensorFlow Lite Micro model for gesture recognition on an ESP32 using an I2S microphone can be a rewarding project. By following the steps outlined in this tutorial, you should be able to set up your environment, prepare your model, and run gesture recognition in real time. Experiment with different models and configurations to enhance your project further!