Book Image

Practical Internet of Things with JavaScript

By : Arvind Ravulavaru
Book Image

Practical Internet of Things with JavaScript

By: Arvind Ravulavaru

Overview of this book

In this world of technology upgrades, IoT is currently leading with its promise to make the world a more smarter and efficient place. This book will show you how to build simple IoT solutions that will help you to understand how this technology works. We would not only explore the IoT solution stack, but we will also see how to do it with the world’s most misunderstood programming language - JavaScript. Using Raspberry Pi 3 and JavaScript (ES5/ES6) as the base to build all the projects, you will begin with learning about the fundamentals of IoT and then build a standard framework for developing all the applications covered in this book. You will then move on to build a weather station with temperature, humidity and moisture sensors and further integrate Alexa with it. Further, you will build a smart wearable for understanding the concept of fall detection. You will then extend it with the 'If This Then That' (IFTTT) rules engine to send an email on fall detection. Finally, you will be working with the Raspberry Pi 3 camera module and surveillance with a bit of facial detection using Amazon Rekognition platform. At the end of the book, you will not only be able to build standalone exciting IoT applications but also learn how you can extend your projects to another level.
Table of Contents (17 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Dedication
Preface

Fall detection


In Chapter 6, Smart Wearable, we gathered three axis values from the accelerometer. Now, we are going to make use of this data to detect falls.

I would recommend watching the video Accelerometer in Freefall (https://www.youtube.com/watch?v=-om0eTXsgnY), which explains how an accelerometer behaves both when it is stationary and in motion.

Now that we understand the basic concept of fall detection, let's talk about our specific use case.

The biggest challenge in fall detection is to distinguish falling from other activities, such as running and jumping. In this chapter, we are going to keep things simple and work on very basic conditions, where a user at rest or in constant motion suddenly falls down.

To identify whether the user has fallen down, we use the signal magnitude vector or SMV. SMV is the root mean square of the values of the three axes. That is:

If we start plotting the SMV over Time for a user who is standing idle and then falls down, we will end up with a graph, as...