Distributed continuous action recognition using a hidden markov model in body sensor networks

Eric Guenterberg, Hassan Ghasemzadeh, Vitali Loseu, Roozbeh Jafari

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

23 Scopus citations

Abstract

One important application of Body Sensor Networks is action recognition. Action recognition often implicitly requires partitioning the sensor data into intervals, then labeling the partitions according to the actions each represents or as a non-action. The temporal partitioning stage is called segmentation and the labeling is called classification. While many effective methods exist for classification, segmentation remains problematic. We present a technique inspired by continuous speech recognition that combines segmentation and classification using Hidden Markov Models. This technique is distributed and only involves limited data sharing between sensor nodes. We show the results of this technique and the bandwidth savings over full data transmission.

Original languageEnglish (US)
Title of host publicationDistributed Computing in Sensor Systems - 5th IEEE International Conference, DCOSS 2009, Proceedings
Pages145-158
Number of pages14
DOIs
StatePublished - 2009
Externally publishedYes
Event5th IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS 2009 - Marina del Rey, CA, United States
Duration: Jun 8 2009Jun 10 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5516 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS 2009
Country/TerritoryUnited States
CityMarina del Rey, CA
Period6/8/096/10/09

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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