5 TinyML Projects for Beginners (Boost Your Portfolio!)

TinyML is revolutionizing the way we build intelligent devices.

By running machine learning models on microcontrollers, TinyML enables powerful applications. From smart homes to industrial monitoring, TinyML is driving innovation across sectors. With a market size projected to reach $2.5 billion by 2030, TinyML is rapidly gaining traction. [1]

TinyML offers beginners an exciting opportunity to learn ML and embedded systems. As a beginner, you can get started with these five exciting projects.

Through hands-on projects, you can gain valuable skills for the future of edge computing.

Project 1: Gesture Recognition with Arduino Nano 33 BLE Sense

Gesture recognition is an exciting application of TinyML that allows devices to interpret and respond to human hand movements. 

The Arduino Nano 33 BLE Sense board is the perfect platform for beginners to explore this fascinating field. Equipped with a built-in Inertial Measurement Unit (IMU) sensor, it can detect orientation, motion, and vibration.

To build a gesture recognition system, follow these steps:

1. Collect training data by performing various hand gestures while recording the IMU sensor readings.
2. Use the collected data to train a machine learning model, such as a neural network, to classify different gestures.
3. Platforms like Edge Impulse make creating and training models accessible without prior machine learning experience.
4. Deploy the trained model to the Arduino board for real-time inference.

Once the model is deployed, you can:

• Connect LEDs to the board's GPIO pins
• Program the LEDs to light up or change colors based on the detected gestures
• Control your devices with a simple wave of your hand!

This project covers the essential stages of the TinyML workflow:

• Data collection
• Model training
• Deployment

It's a great starting point for gaining practical experience and appreciating TinyML's potential in interactive applications. Are you ready to give your projects a new dimension of interaction?

Project 2: Person Detection with ESP32-CAM

Person detection is a powerful computer vision application with numerous use cases, from security systems to smart home automation. The ESP32-CAM board, with its integrated camera module, is an ideal choice for building a person detection system using TinyML.

Here's how you can create your person-detection project:

1. Set up the ESP32-CAM board and connect it to your computer.
2. Collect a dataset of images containing people and without people to train your model.
3. Use a tool like Edge Impulse to label the pictures and create an object detection model.
4. Train the model using the labelled dataset and optimize it for the ESP32-CAM.
5. Deploy the trained model to the ESP32-CAM and run inference on the live camera feed.

Once the model is running on the ESP32-CAM, you can:

• Detect the presence of people in real-time
• Trigger an alarm or send a notification when a person is detected
• Integrate the system with other devices or applications for a wide range of use cases

This project introduces you to computer vision and more complex model architectures like object detection. You'll learn how to:

• Work with image data and cameras
• Label datasets for object detection tasks
• Train and optimize models for resource-constrained devices
• Run inference on live camera feeds

By completing this project, you'll gain valuable experience applying TinyML to real-world problems and discover the potential of computer vision on the edge.

Project 3: Sound Classification with Raspberry Pi Pico

Sound classification is a fascinating application of TinyML that enables devices to understand and respond to their auditory environment. You can build a compact and efficient sound classification system with the Raspberry Pi Pico and its powerful RP2040 microcontroller.

Here's a step-by-step guide to creating your sound classifier:

1. Set up the Raspberry Pi Pico and connect a microphone to capture audio.
2. Record audio samples of sounds you want to classify, such as applause, dog barks, or music.
3. Python libraries like librosa or pyAudioAnalysis can extract relevant features from the audio samples.
4. Build an audio classification model using machine learning algorithms like Support Vector Machines (SVM) or Convolutional Neural Networks (CNN).
5. Train the model on your labelled audio dataset, optimizing hyperparameters for the best performance.
6. Convert the trained model to TensorFlow Lite format for deployment on the Pico.
7. Write a Python script to load the model, capture real-time audio, and perform inference on the Pico.

Through this project, you'll gain valuable insights into:

• Working with audio data and understanding its characteristics
• Extracting meaningful features from raw audio signals
• Building and training machine learning models for classification tasks
• Optimizing models for resource-constrained devices like microcontrollers
• Deploying models for real-time inference on the edge

Sound classification opens up possibilities, from smart home automation to industrial monitoring. Imagine your Pico-powered device recognizing and responding to specific sounds, triggering actions, or sending alerts based on the auditory events it detects.

By completing this project, you'll have a solid foundation in audio processing, feature extraction, and classification using TinyML techniques. You'll be equipped with the skills to tackle more advanced audio applications and explore the exciting field of audio intelligence on the edge.

Project 4: Temperature Prediction with Wio Terminal

Temperature prediction is a practical application of TinyML that can be used in various domains, from weather forecasting to industrial monitoring. The Wio Terminal and a temperature and humidity sensor provide an excellent platform for building a temperature prediction system.

Here's a step-by-step guide to creating your own temperature prediction project:

1. Use the Grove connector to connect a temperature and humidity sensor, such as the DHT11 or DHT22, to the Wio Terminal.
2. Collect a temperature and humidity readings dataset over time, ensuring that you have sufficient data points to train your model.
3. Preprocess the collected data by normalizing the temperature and humidity values and splitting the dataset into training and testing sets.
4. Build a regression model using machine learning algorithms like Linear Regression, Decision Trees, or Neural Networks.
5. Train the model on the collected dataset, tuning hyperparameters for the best performance.
6. Convert the trained model to TensorFlow Lite format for deployment on the Wio Terminal.
7. Write an Arduino sketch to read sensor data, perform inference using the trained model, and display the predicted temperature on the Wio Terminal's LCD.

This project introduces you to several key concepts:

• Interfacing with sensors to collect real-world data
• Preprocessing and preparing data for machine learning tasks
• Building and training regression models for continuous value prediction
• Deploying models on resource-constrained devices
• Visualizing predictions on the Wio Terminal's display

By completing this project, you'll gain hands-on experience in applying TinyML to solve real-world problems. You'll learn how to:

• Integrate sensors with the Wio Terminal
• Collect and preprocess data for model training
• Train and optimize regression models
• Deploy models for inference on the edge
• Visualize data and predictions using the Wio Terminal's LCD

Temperature prediction has numerous applications, from smart home automation to industrial process control. 

With the skills acquired through this project, you'll be well-equipped to tackle more complex prediction tasks and build intelligent edge devices that can make decisions based on real-time sensor data.

Project 5: Anomaly Detection with Arduino Portenta H7

Anomaly detection is a critical application of TinyML in predictive maintenance and condition monitoring. By identifying unusual patterns or deviations from normal behavior, potential issues can be detected before they lead to costly failures.

The Arduino Portenta H7, with its powerful dual-core processor and onboard sensors, is an ideal platform for building an anomaly detection system.

Here's how you can create your own anomaly detection project:

1. Connect an accelerometer to the Arduino Portenta H7 to capture vibration data from a motor or machine.
2. Collect a dataset of standard vibration patterns during the machine's healthy operation.
3. Preprocess the collected data by normalizing and segmenting the accelerometer readings into fixed-length windows.
4. Build an autoencoder model using deep learning frameworks like TensorFlow or PyTorch. Autoencoders are unsupervised learning models that learn to reconstruct input data, effectively capturing the underlying patterns.
5. Train the autoencoder on the average vibration data, minimizing the reconstruction error.
6. Convert the trained model to TensorFlow Lite format for deployment on the Portenta H7.
7. Write an Arduino sketch to read accelerometer data, perform inference using the trained autoencoder, and calculate the reconstruction error.
8. Set a threshold for the reconstruction error, above which an anomaly is detected, indicating potential issues with the motor or machine.

This project delves into more advanced TinyML concepts:

• Working with time-series data from sensors
• Building and training autoencoder models for anomaly detection
• Deploying models on high-performance microcontrollers like the Portenta H7
• Implementing real-time anomaly detection on the edge

By completing this project, you'll gain valuable experience in:

• Collecting and preprocessing time-series data
• Designing and training autoencoder models
• Optimizing models for resource-constrained devices
• Implementing anomaly detection algorithms on the edge
• Integrating TinyML into predictive maintenance applications

Anomaly detection has wide-ranging applications, from industrial machinery to intelligent city infrastructure. With the skills acquired through this project, you'll be well-prepared to tackle complex anomaly detection challenges and build intelligent systems that can identify and respond to unusual events in real-time.

This project showcases TinyML's power to enable advanced analytics and decision-making on the edge, opening up new possibilities for predictive maintenance, quality control, and beyond.

Final Words

When it comes to TinyML, the possibilities are truly endless. 

These five projects are just the tip of the iceberg, but they showcase the incredible potential of this technology. From gesture recognition to anomaly detection, TinyML is revolutionizing how we interact with and understand the world around us.

But here's the thing: you don't need to be an expert to start with TinyML. These projects are designed with beginners in mind, providing a hands-on way to learn the fundamentals of machine learning and embedded systems. Sure, there may be a learning curve, but the sense of accomplishment you'll feel when you see your projects come to life is unbeatable.

So why not give it a shot? Roll up your sleeves, grab a microcontroller, and dive in. You might surprise yourself with what you're capable of creating. And who knows? Your TinyML projects could be the start of something big - a new career, a groundbreaking invention, or simply a fun and rewarding hobby.

The future of edge computing is bright, and you can shape it with TinyML. Don't be afraid to take that first step. Embrace the challenge, learn from your mistakes, and, most importantly, have fun!