Discovery of Activity Patterns using Topic Models Paper by Tm Hunh, - - PowerPoint PPT Presentation

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Discovery of Activity Patterns using Topic Models Paper by Tm Hunh, - - PowerPoint PPT Presentation

Oct 09, 2022 110 likes •323 views

Discovery of Activity Patterns using Topic Models Paper by Tm Hunh, Mario Fritz and Bernt Schiele Presentation by Roland Meyer 2 Introduction Detect routines based on body movement Complex due to large variations in activities 3

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Discovery of Activity Patterns using Topic Models

Paper by Tâm Huỳnh, Mario Fritz and Bernt Schiele Presentation by Roland Meyer

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Introduction

  • Detect routines based on body movement
  • Complex due to large variations in activities

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Contributions

  • New method to recognize daily routines
  • Reusing an established method from text processing
  • Applicable without user annotation

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Topic Models

  • Used for text processing for classification
  • Collection of words (“Bag-of-words”)
  • Unsupervised

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Topic Models

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Daily Routine Modeling

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Data collection

  • 1 person
  • 16 days
  • 2 wearable sensors
  • Accelerometer
  • Realtime clock
  • 4 hours of memory

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Annotation

  • Online annotation
  • Periodic set of questions on cell phone
  • Time diary
  • Occasional snapshots
  • Offline annotation
  • User could correct / complement data
  • Used as ground truth

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Discovering activities

  • 34 distinct activities
  • Mean, variance, frequency from acceleration sensors
  • Combined with time-of-day
  • SVMs, HMMs, Naive Bayes evaluated as classifiers
  • 72.7% accuracy
  • Great variations
  • Problems with short and similar tasks

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Discovering topics

  • Latent Dirichlet Allocation on activity data
  • Sliding window of 30 min. over activity stream
  • 10 topics

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Discovering topics

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Results on Discovering topics

  • Precision and recall calculated for 6 of 7 day to cross-

validate results

  • Supervised classifier using HMMs to calculate baseline

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Unsupervised learning

  • Get rid of user annotations
  • Labels from data clustering

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Future work

  • Semi-supervision
  • Noise modeling
  • Include location information
  • More users with more diverse lives
  • Build applications
  • Use better sensors (more memory)

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Including location

  • “Discovering Daily Routines from Google Latitude with Topic Models”

by Laura Ferrari and Marco Mamei

  • “Discovering Human Routines from Cell Phone Data with Topic Models”

by Katayoun Farrahi and Daniel Gatica-Perez

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Including location

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“Discovering Daily Routines from Google Latitude with Topic Models” - Laura Ferrari and Marco Mamei

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Including location

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“Discovering Daily Routines from Google Latitude with Topic Models” - Laura Ferrari and Marco Mamei

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Including location

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“Discovering Human Routines from Cell Phone Data with Topic Models” - Katayoun Farrahi and Daniel Gatica-Perez

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Reviews

  • Average score: 1.75 (accept)
  • Solid ground truth
  • Privacy not addressed
  • Spelling errors, graphs badly placed
  • No automation, data needs to be manually copied

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