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TinyML Cookbook

You're reading from TinyML Cookbook Combine artificial intelligence and ultra-low-power embedded devices to make the world smarter

Product type Paperback

Published in Apr 2022

Publisher Packt

ISBN-13 9781801814973

Length 344 pages

Edition 1st Edition

Tools

Combine

Concepts

Artificial Intelligence

Author (1):

Gian Marco Iodice

View More author details

Table of Contents (10) Chapters

Preface

1. Chapter 1: Getting Started with TinyML

2. Chapter 2: Prototyping with Microcontrollers FREE CHAPTER

3. Chapter 3: Building a Weather Station with TensorFlow Lite for Microcontrollers

4. Chapter 4: Voice Controlling LEDs with Edge Impulse

5. Chapter 5: Indoor Scene Classification with TensorFlow Lite for Microcontrollers and the Arduino Nano

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

7. Chapter 7: Running a Tiny CIFAR-10 Model on a Virtual Platform with the Zephyr OS

8. Chapter 8: Toward the Next TinyML Generation with microNPU

9. Other Books You May Enjoy

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...

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Authors (1)

Gian Marco Iodice

Gian Marco Iodice

Gian Marco Iodice is team and tech lead in the Machine Learning Group at Arm, who co-created the Arm Compute Library in 2017. The Arm Compute Library is currently the most performant library for ML on Arm, and it's deployed on billions of devices worldwide – from servers to smartphones. Gian Marco holds an MSc degree, with honors, in electronic engineering from the University of Pisa (Italy) and has several years of experience developing ML and computer vision algorithms on edge devices. Now, he's leading the ML performance optimization on Arm Mali GPUs. In 2020, Gian Marco cofounded the TinyML UK meetup group to encourage knowledge-sharing, educate, and inspire the next generation of ML developers on tiny and power-efficient devices.

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