When the ESP32 Development Board Meets TinyML In the intersection of the Internet of Things and artificial intelligence, the TinyML-ESP32 project has emerged as a dark horse! Supported by the Black Walnut Laboratory, this open-source project maximizes the performance of the ESP32-WROOM-32 development board, integrating hardware such as gyroscopes, microphones, and LED light groups to achieve gesture recognition, voice wake-up, and motion counting as three major AI superpowers. No cloud computing is needed; machine learning inference is completed directly on the device, making it a textbook case of edge computing!
Gesture Recognition: From Data Collection to Model Deployment The project captures motion trajectories in three-dimensional space in real-time using the GY-25Z gyroscope and parses sensor data with the ArduinoJson library. After the developer collects data for 100 specific gestures, the system automatically triggers the TensorFlow Lite model training process. The trained model occupies only a few tens of KB of storage space but can achieve millisecond-level response—waving left turns the LED red immediately; waving right switches it to blue, and the entire process flows smoothly. Even more impressively, with the arduinoWebSockets library, prediction results can be synchronized in real-time to a web visualization interface!
Hotword Wake-Up: Letting the Device Understand Your Voice The built-in Inter-IC Sound microphone module of the project, combined with the arduinoFFT library, enables real-time audio spectrum analysis. When the user says a preset keyword, the system matches voiceprint features through the TensorFlow Lite model, achieving an accuracy rate of over 95%. During the training phase, Jupyter Notebook is used for waveform visualization and data augmentation, allowing for precise recognition even in the presence of background noise through frequency domain feature extraction. When successfully awakened, the WS2812 LED light group presents a dynamic flowing light effect, maximizing the technological feel!
Jump Rope Counter: AI Empowering Sports Health Behind this seemingly simple function lies the data magic of a six-axis attitude sensor. The project obtains acceleration and angular velocity data in real-time through the ESP32’s UART interface, capturing the unique waveform characteristics of jump rope actions using a sliding window algorithm. A contrastive learning strategy is used during model training to effectively distinguish jump rope actions from other daily movements. In actual tests, the counting error for 100 consecutive jumps does not exceed 2, and the data is pushed to the mobile end in real-time via WebSocket, making the sports data dashboard comparable to professional fitness equipment!
Open Source Ecosystem: Six Core Libraries Joining Forces The power of the project relies on the support of the open-source community:
<span>espressif/arduino-esp32</span>
Provides low-level hardware drivers <span>Adafruit_NeoPixel</span>
Achieves cool LED light control <span>TensorFlowLite_ESP32</span>
Hosts the machine learning inference engine <span>arduinoWebSockets</span>
Connects the device with the cloud for data transmission <span>ArduinoJson</span>
Efficiently processes sensor data streams <span>arduinoFFT</span>
Completes real-time Fourier transform
Conclusion The TinyML-ESP32 project perfectly exemplifies “small size, big energy”—achieving AI tasks traditionally requiring GPU servers on a development board the size of a fingernail. Whether it’s gesture control in smart homes, developing voice interaction devices, or monitoring sports health, this project provides out-of-the-box solutions. Even more exciting, all code and trained models are completely open-source, allowing developers to customize their own edge AI applications!
Project Address:https://github.com/HollowMan6/TinyML-ESP32