Book Image

TinyML Cookbook

By : Gian Marco Iodice
Book Image

TinyML Cookbook

By: Gian Marco Iodice

Overview of this book

This book explores TinyML, a fast-growing field at the unique intersection of machine learning and embedded systems to make AI ubiquitous with extremely low-powered devices such as microcontrollers. The TinyML Cookbook starts with a practical introduction to this multidisciplinary field to get you up to speed with some of the fundamentals for deploying intelligent applications on Arduino Nano 33 BLE Sense and Raspberry Pi Pico. As you progress, you’ll tackle various problems that you may encounter while prototyping microcontrollers, such as controlling the LED state with GPIO and a push-button, supplying power to microcontrollers with batteries, and more. Next, you’ll cover recipes relating to temperature, humidity, and the three “V” sensors (Voice, Vision, and Vibration) to gain the necessary skills to implement end-to-end smart applications in different scenarios. Later, you’ll learn best practices for building tiny models for memory-constrained microcontrollers. Finally, you’ll explore two of the most recent technologies, microTVM and microNPU that will help you step up your TinyML game. By the end of this book, you’ll be well-versed with best practices and machine learning frameworks to develop ML apps easily on microcontrollers and have a clear understanding of the key aspects to consider during the development phase.
Table of Contents (10 chapters)

Chapter 6: Building a Gesture-Based Interface for YouTube Playback

Gesture recognition is a technology that interprets human gestures to allow people to interact with their devices without touching buttons or displays. This technology is now in various consumer electronics (for example, smartphones and game consoles) and involves two principal ingredients: a sensor and a software algorithm.

In this chapter, we will show you how to use accelerometer measurements in conjunction with machine learning (ML) to recognize three hand gestures with the Raspberry Pi Pico. These recognized gestures will then be used to play/pause, mute/unmute, and change YouTube videos on our PC.

We will start by collecting the accelerometer data to build the gesture recognition dataset. In this part, we will learn how to interface with the I2C protocol and use the Edge Impulse data forwarder tool. Next, we will focus on the Impulse design, where we will build a spectral-features-based fully connected neural...