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Modeling Activity Tracker Data Using Deep Boltzmann Machines.

Martin Treppner1, Stefan Lenz1, Harald Binder1

  • 1Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg.

Studies in Health Technology and Informatics
|August 28, 2018
PubMed
Summary
This summary is machine-generated.

Deep Boltzmann machines (DBMs) can model Fitbit activity tracker data, revealing distinct user patterns. This unsupervised learning approach is feasible for analyzing large, unlabeled datasets in health research.

Keywords:
Activity TrackersDeep Boltzmann MachinesDeep Learning

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Area of Science:

  • Health Informatics
  • Machine Learning
  • Wearable Technology

Background:

  • Commercial activity trackers are increasingly prevalent, generating large volumes of unlabeled health data.
  • Analyzing this data presents challenges for traditional statistical modeling.

Purpose of the Study:

  • To investigate the feasibility of deep learning for unsupervised learning with activity tracker data.
  • To explore weekly usage patterns using Deep Boltzmann Machines (DBMs).

Main Methods:

  • Utilized Deep Boltzmann Machines (DBMs), a generative deep learning approach.
  • Preprocessed Fitbit activity tracker data for compatibility with binary DBMs.
  • Examined weekly usage patterns through unsupervised learning.

Main Results:

  • Identified two distinct user activity patterns.
  • One group showed high tracker usage on Mondays and Tuesdays.
  • Another group used trackers consistently throughout the week.

Conclusions:

  • Deep Boltzmann Machines (DBMs) are a feasible and potentially valuable tool for modeling activity tracker data.
  • Unsupervised learning with DBMs can uncover meaningful patterns in large, unlabeled wearable sensor datasets.