Introduction

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.

Keywords

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

Authors and affiliations

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

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-66308-1
  • 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

Document: https://link.springer.com/book/10.1007/978-3-319-66308-1#about

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