Tiny Machine Learning (TinyML) has opened the door for powerful AI models to run directly on microcontrollers, bringing intelligent capabilities to everyday embedded projects. The ESP32 WROVER kit, with its ample RAM and built-in Wi-Fi, is a fantastic platform for experimenting with TinyML. Paired with common Arduino-compatible sensors, it can become the heart of practical, low-power AI systems.
In this post, we’ll explore a progression of project ideas that build both your TinyML skills and your embedded systems knowledge. The sequence starts with simple, single-sensor applications and grows toward complex, multi-sensor, real-world deployments.
Building Foundations with Simple Models
A great place to begin is with a voice-controlled RGB LED. Using an INMP441 I²S microphone, you can train a keyword spotting model to recognize a handful of words such as “red,” “blue,” and “off.” These keywords control the onboard RGB LED, giving you instant feedback. This project teaches audio preprocessing, MFCC feature extraction, and how to deploy small speech models onto the ESP32. It’s the “Hello World” of TinyML, and once you get it working, you’ll feel the excitement of real-time AI running on a tiny device.
Another foundational step is creating a smart motion detector with a PIR sensor. Here, the model classifies motion events as human or non-human (for example, pet movement). It’s an opportunity to experiment with binary classification from simple sensor data while learning to manage limited memory and computational resources.
Moving Into More Complex Data
Once you’re comfortable with the basics, motion sensing with an accelerometer unlocks more possibilities. An MPU6050 or ADXL345 accelerometer can capture gesture data, letting you classify actions like “shake,” “tap,” or “circle.” This introduces you to sliding windows, temporal data analysis, and motion data augmentation.
The skills you build here can also be applied to environmental monitoring. A smart plant monitor uses a capacitive soil moisture sensor, DHT22 temperature/humidity sensor, and a light sensor to predict when a plant needs water. This is a great introduction to regression models in TinyML and sensor fusion, where multiple inputs are combined to produce a single output.
Tackling Noise and Real-World Variability
In real-world environments, noise is inevitable, and learning to handle it is an important milestone. A sound-based security system, for example, can be trained to detect specific audio events like glass breaking or door knocks. By collecting a varied dataset that includes both target sounds and background noise, you’ll learn how to make models robust and reliable.
Environmental anomaly detection is another challenge worth exploring. By logging readings from gas sensors, temperature sensors, and humidity sensors, you can train an anomaly detection model such as an autoencoder. The system learns what “normal” conditions look like, then flags unusual events, such as sudden spikes that might indicate a gas leak. This is a great way to experiment with unsupervised learning on a microcontroller.
Pushing the Limits of the ESP32 WROVER
Once you’ve mastered small models and noise handling, you can push the ESP32 WROVER into more demanding tasks. A smart traffic light system using the ESP32-CAM module and an OV2640 camera can detect vehicles or pedestrians and adjust light states accordingly. This is where you’ll work with low-resolution images, aggressive quantization, and highly optimized neural networks to fit vision models onto a microcontroller.
Finally, a wearable posture coach combines motion sensors and low-power continuous inference to detect poor posture. This type of project challenges you to optimize for battery life, integrate Bluetooth for notifications, and handle variability between users. It’s a practical application that could evolve into a commercially viable product.
Growing Your Skills Step by Step
Each of these projects teaches a distinct set of TinyML skills, from working with different sensor modalities to implementing classification, regression, and anomaly detection. The gradual progression ensures that you not only learn to train and deploy models, but also understand the nuances of embedded data collection, noise handling, and system optimization. By the time you reach the more advanced projects, you’ll have worked with audio, motion, environmental, and vision data. You’ll have applied supervised, unsupervised, and regression techniques. And most importantly, you’ll have gained the experience to design your own intelligent systems from the ground up.
If you follow this path with the ESP32 WROVER and a handful of affordable Arduino sensors, you’ll not only deepen your understanding of TinyML, but you’ll also build a portfolio of real, functional AI-powered devices that you can demonstrate, improve, and share with the maker community.