A mining technique using n-grams and motion transcripts for body sensor network data repository

Vitali Loseu, Hassan Ghasemzadeh, Latifur R. Khan, Roozbeh Jafari

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Recent years have seen a large influx of applications in the field of Body Sensor Networks (BSN). BSN, and in general wearable computers with sensors, can give researchers, users or clinicians access to tremendously valuable information extracted from data that were collected in users' natural environment. With this information, one can monitor the progression of a disease, identify its early onset or simply assess user's wellness. One major obstacle is managing repositories that store large amounts of BSN data. To address this issue, we propose a data mining approach for large BSN data repositories. We represent sensor readings with motion transcripts that maintain structural properties of the signal. To further take advantage of the signal's structure, we define a data mining technique using n-grams. We reduce overwhelmingly large number of n-grams via information gain (IG) feature selection. We report the effectiveness of our approach in terms of the speed of mining while maintaining an acceptable accuracy in terms of precision and recall. We demonstrate that the system can achieve average 99% precision with an average 100% recall on our pilot data with the help of only one transition for each movement.

Original languageEnglish (US)
Title of host publicationProceedings - Wireless Health 2010, WH'10
Number of pages10
StatePublished - 2010
Externally publishedYes
Event1st Wireless Health Conference, WH'10 - San Diego, CA, United States
Duration: Oct 5 2010Oct 7 2010

Publication series

NameProceedings - Wireless Health 2010, WH'10


Other1st Wireless Health Conference, WH'10
Country/TerritoryUnited States
CitySan Diego, CA


  • Patricia tree
  • body sensor networks
  • data mining
  • n-grams
  • string templates

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management


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