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Machine Learning with Swift

You're reading from   Machine Learning with Swift Artificial Intelligence for iOS

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781787121515
Length 378 pages
Edition 1st Edition
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Authors (3):
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Jojo Moolayil Jojo Moolayil
Author Profile Icon Jojo Moolayil
Jojo Moolayil
Oleksandr Baiev Oleksandr Baiev
Author Profile Icon Oleksandr Baiev
Oleksandr Baiev
Alexander Sosnovshchenko Alexander Sosnovshchenko
Author Profile Icon Alexander Sosnovshchenko
Alexander Sosnovshchenko
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Toc

Table of Contents (14) Chapters Close

Preface 1. Getting Started with Machine Learning FREE CHAPTER 2. Classification – Decision Tree Learning 3. K-Nearest Neighbors Classifier 4. K-Means Clustering 5. Association Rule Learning 6. Linear Regression and Gradient Descent 7. Linear Classifier and Logistic Regression 8. Neural Networks 9. Convolutional Neural Networks 10. Natural Language Processing 11. Machine Learning Libraries 12. Optimizing Neural Networks for Mobile Devices 13. Best Practices

Recognizing human motion using KNN


Core Motion is an iOS framework that provides an API for inertial sensors of mobile devices. It also recognizes some user motion types, and stores them to the HealthKit database.

Note

If you are not familiar with Core Motion API, please check the framework reference: https://developer.apple.com/reference/coremotion. The code for this example can be found in the Code/02DistanceBased/ MotionClassification folder of supplementary materials.

As per iOS 11 beta 2, the CMMotionActivity class includes the following types of motion:

  • Stationary
  • Walking
  • Running
  • Automotive
  • Cycling

Everything else falls into an unknown category or is recognized as one of the preceding. Core Motion doesn't provide a way to recognize custom motion types so we'll train our own classifier for this purpose. Unlike decision trees from the previous chapter, KNN will be trained on device end-to-end. It will also not be frozen inside Core ML because as we keep all the control on it, we'll be able to...

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