Physical movement monitoring using body sensor networks: A phonological approach to construct spatial decision trees

Hassan Ghasemzadeh, Roozbeh Jafari

Research output: Contribution to journalArticlepeer-review

83 Scopus citations


Monitoring human activities using wearable sensor nodes has the potential to enable many useful applications for everyday situations. Limited computation, battery lifetime and communication bandwidth make efficient use of these platforms crucial. In this paper, we introduce a novel classification model that identifies physical movements from body-worn inertial sensors while taking collaborative nature and limited resources of the system into consideration. Our action recognition model uses a decision tree structure to minimize the number of nodes involved in classification of each action. The decision tree is constructed based on the quality of action recognition in individual nodes. A clustering technique is employed to group similar actions and measure quality of per-node identifications. We pose an optimization problem for finding a minimal set of sensor nodes contributing to the action recognition. We then prove that this problem is NP-hard and provide fast greedy algorithms to approximate the solution. Finally, we demonstrate the effectiveness of our distributed algorithm on data collected from five healthy subjects. In particular, our system achieves a 72.4% reduction in the number of active nodes while maintaining 93.3% classification accuracy.

Original languageEnglish (US)
Article number5661882
Pages (from-to)66-77
Number of pages12
JournalIEEE Transactions on Industrial Informatics
Issue number1
StatePublished - Feb 2011
Externally publishedYes


  • Action recognition
  • body sensor networks
  • collaborative signal processing
  • decision tree model
  • phonology
  • primitive

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering


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