Machine Learning for the Quantified Self

On the Art of Learning from Sensory Data

  • Mark Hoogendoorn
  • Burkhardt Funk

Part of the Cognitive Systems Monographs book series (COSMOS, volume 35)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Mark Hoogendoorn, Burkhardt Funk
    Pages 1-12
  3. Sensory Data and Features

    1. Front Matter
      Pages 13-13
    2. Mark Hoogendoorn, Burkhardt Funk
      Pages 15-24
    3. Mark Hoogendoorn, Burkhardt Funk
      Pages 25-50
    4. Mark Hoogendoorn, Burkhardt Funk
      Pages 51-70
  4. Learning Based on Sensory Data

    1. Front Matter
      Pages 71-71
    2. Mark Hoogendoorn, Burkhardt Funk
      Pages 73-100
    3. Mark Hoogendoorn, Burkhardt Funk
      Pages 101-121
    4. Mark Hoogendoorn, Burkhardt Funk
      Pages 123-165
    5. Mark Hoogendoorn, Burkhardt Funk
      Pages 167-202
    6. Mark Hoogendoorn, Burkhardt Funk
      Pages 203-214
  5. Discussion

    1. Front Matter
      Pages 215-215
    2. Mark Hoogendoorn, Burkhardt Funk
      Pages 217-221
  6. Back Matter
    Pages 223-231

About this book


This book explains the complete loop to effectively use self-tracking data for machine learning. While it focuses on self-tracking data, the techniques explained are also applicable to sensory data in general, making it useful for a wider audience. Discussing concepts drawn from state-of-the-art scientific literature, it illustrates the approaches using a case study of a rich self-tracking data set. Self-tracking has become part of the modern lifestyle, and the amount of data generated by these devices is so overwhelming that it is difficult to obtain useful insights from it. Luckily, in the domain of artificial intelligence there are techniques that can help out: machine-learning approaches allow this type of data to be analyzed. While there are sample books that explain machine-learning techniques, self-tracking data comes with its own difficulties that require dedicated techniques such as learning over time and across users.


Cognitive Systems Machine Learning Quantified Self Learning from Sensory Data Personalized m-health

Authors and affiliations

  • Mark Hoogendoorn
    • 1
  • Burkhardt Funk
    • 2
  1. 1.Department of Computer ScienceVrije Universiteit AmsterdamAmsterdamThe Netherlands
  2. 2.Institut für WirtschaftsinformatikLeuphana Universität LüneburgLüneburgGermany

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-319-66307-4
  • Online ISBN 978-3-319-66308-1
  • Series Print ISSN 1867-4925
  • Series Online ISSN 1867-4933
  • Buy this book on publisher's site
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